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GP trajectory (STEAM)",2014,"2D laser rangefinder (range\u002Fbearing to tube landmarks) | wheel odometry","lidar | wheel_or_leg_odometry | other | platform | compute",{"id":519,"shortName":520,"year":521,"sensors":522,"equipment":523},"sgraphsplus2023","S-Graphs+",2023,"3D LiDAR (VLP-16 data used in all datasets, Sec. VI-A) | Odometry input: robot encoders on the in-house real data (the in-house platform is a legged robot, Fig. 1); LiDAR odometry (VGICP or FLOAM) on simulated and TIERS data","lidar | wheel_or_leg_odometry | platform",{"id":525,"shortName":526,"year":527,"sensors":528,"equipment":529},"suma2018","SuMa",2018,"3D LiDAR (Velodyne HDL-64E S2 via KITTI)","lidar | gnss | compute",{"id":531,"shortName":532,"year":533,"sensors":534,"equipment":535},"biber2003ndt","NDT (2D)",2003,"[\"SICK 2D laser scanner, 180 deg field of view, 1 deg angular resolution (Sec. VIII)\"]","lidar | compute",{"id":537,"shortName":538,"year":539,"sensors":540,"equipment":541},"molalo2025","MOLA-LO",2025,"3D LiDAR (16 to 128 rings) | 2D LiDAR | optional wheel odometry for kinematic prediction | optional consumer-grade GNSS (loop closure and georeferencing only) | IMU not used by LO","lidar | gnss | platform | tls_scanner | wheel_or_leg_odometry | other | compute",{"id":543,"shortName":544,"year":545,"sensors":546,"equipment":547},"rovio2015","ROVIO",2015,"monocular camera | IMU","stereo_camera | imu | other | platform | compute",{"id":549,"shortName":550,"year":551,"sensors":552,"equipment":553},"blum2021precisebim","Localization in architectural 3D plans",2021,"3D LiDAR | 3 cameras | IMU","platform | lidar | camera | imu | other | total_station",{"id":555,"shortName":556,"year":539,"sensors":557,"equipment":558},"okvis2x2025","OKVIS2-X","one or more cameras | IMU | optional learned depth (stereo and multi-view stereo networks) | optional LiDAR | optional GNSS","compute | stereo_camera | other | camera | lidar | gnss | imu",{"id":560,"shortName":561,"year":515,"sensors":562,"equipment":563},"borrmann2014thermalmapping","Irma3D automated thermal 3D mapping","terrestrial 3D laser scanner (Riegl VZ-400) | thermal camera (optris PI160) | colour webcam (Logitech QuickCam Pro 9000) | 2D laser scanner (SICK LMS100) for obstacle avoidance","tls_scanner | thermal | camera | lidar | platform | other",{"id":565,"shortName":566,"year":515,"sensors":567,"equipment":568},"bosche2014flatness","TLS + BIM floor flatness control","terrestrial laser scanner (FARO Focus3D)","tls_scanner | other",{"id":570,"shortName":571,"year":572,"sensors":573,"equipment":248},"bosche2010asbuiltdims","Scan-vs-BIM object recognition and as-built dimensions",2010,"terrestrial laser scanner (Trimble GX 3D per Sec. 1.1.2 and ref. [44])",{"id":381,"shortName":382,"year":575,"sensors":576,"equipment":577},2009,"encoder on the spinning mount, described as accurate, giving each 2D scan's pose relative to the vehicle","lidar | other | platform | compute",{"id":384,"shortName":385,"year":579,"sensors":580,"equipment":581},2012,"2D time-of-flight laser Hokuyo UTM-30LX (270 deg FoV, 30 m maximum range, 40 Hz) (Sec. II) | Industrial-grade MEMS IMU MicroStrain 3DM-GX2 at 100 Hz with a rotational rate range of at least 600 deg\u002Fs; the second-generation device uses a MicroStrain 3DM-GX3 (Sec. II, IV-D)","lidar | imu | other | mobile_scanner_device | platform | compute",{"id":583,"shortName":584,"year":539,"sensors":585,"equipment":586},"steamlio2025","STEAM-LIO (GP continuous-time LIO)","3D spinning LiDAR (Velodyne Alpha-Prime 128-beam on Boreas; 64-beam Ouster in Newer College; 64-beam Velodyne in KITTI-raw, LiDAR-only) | IMU (Applanix raw IMU at 200 Hz on Boreas; Ouster internal IMU at 100 Hz in Newer College) | 2D spinning radar (Navtech CIR304-H) for the radar-inertial variant","lidar | radar | gnss | imu | camera | compute | platform",{"id":588,"shortName":589,"year":551,"sensors":590,"equipment":535},"cai2021ikdtree","ikd-Tree","3D LiDAR | IMU (in the FAST-LIO application test)",{"id":592,"shortName":593,"year":551,"sensors":594,"equipment":595},"orbslam3_2021","ORB-SLAM3","monocular camera | stereo (pin-hole or fisheye; rectification not required) | RGB-D (supported by the library; no RGB-D experiment reported) | IMU","compute | stereo_camera | imu | platform",{"id":597,"shortName":598,"year":599,"sensors":600,"equipment":601},"pronto2020","Pronto",2020,"IMU (KVH 1750, KVH 1775, 3DM-GX4-25 or Xsens MTi-100 depending on robot) | joint encoders and force or torque sensing for leg odometry | stereo camera (Carnegie Robotics Multisense SL) or RGB-D camera (Intel RealSense D435) for FOVIS visual odometry | LiDAR (Hokuyo UTM-30LX-EW spinning in the Multisense SL, or Velodyne VLP-16 on ANYmal) for AICP registration","imu | stereo_camera | lidar | other | rgbd | total_station | wheel_or_leg_odometry",{"id":603,"shortName":604,"year":509,"sensors":605,"equipment":606},"gvins2022","GVINS","monocular camera (left camera of a VI-Sensor) | IMU | GNSS receiver raw code pseudorange and Doppler (GPS, GLONASS, Galileo, BeiDou)","stereo_camera | imu | gnss | platform | compute",{"id":608,"shortName":609,"year":539,"sensors":610,"equipment":611},"resple2025","RESPLE","one or multiple 3D LiDARs (Ouster OS1-16, Livox Mid70, Mid360, Avia, Hesai XT32) | IMU optional (VN100 or LiDAR built-in IMU)","lidar | imu | platform | other | compute",{"id":613,"shortName":614,"year":615,"sensors":616,"equipment":617},"censi2007covariance","ICP covariance (Censi)",2007,"simulated 2D range finder: 52 rays over 360 deg, zero-mean Gaussian range noise with 0.03 m standard deviation","other",{"id":619,"shortName":620,"year":621,"sensors":622,"equipment":623},"censi2008_plicp","PL-ICP and CSM (laser_scan_matcher)",2008,"2D laser range finder (SICK, 360 rays over 180 deg in the test log)","lidar | platform | compute",{"id":625,"shortName":626,"year":509,"sensors":627,"equipment":628},"lamp2_2022","LAMP 2.0","3D LiDARs (three Velodyne lidars on Husky, a single lidar on Spot; models not reported) | Hovermap payload on some robots | odometry input from LOCUS or Hovermap (front-end agnostic)","platform | lidar | mobile_scanner_device | compute | other",{"id":630,"shortName":631,"year":551,"sensors":632,"equipment":633},"chebrolu2021adaptive","Adaptive robust kernels","3D LiDAR scans of the KITTI odometry benchmark (scanner model not named in the paper) | monocular camera images simulated in CARLA (car front-looking, UAV nadir, strong shadows, side-looking with motion blur)","lidar | camera",{"id":635,"shortName":636,"year":492,"sensors":528,"equipment":637},"sumapp2019","SuMa++","lidar | gnss | camera | compute",{"id":639,"shortName":640,"year":509,"sensors":641,"equipment":642},"dlo2022","DLO","3D LiDAR (Ouster OS1, Velodyne VLP-16) | IMU (optional, gyroscope rotational prior only; VectorNav VN-100 on the field platforms and in the SubT Alpha dataset)","lidar | imu | platform | compute",{"id":644,"shortName":645,"year":509,"sensors":646,"equipment":647},"ndtloam2022","NDT-LOAM","3D LiDAR only (Velodyne HDL-64E in KITTI; horizontal Velodyne VLP-16 on the Kylin backpack)","lidar | gnss | stereo_camera | imu | platform | compute",{"id":649,"shortName":650,"year":521,"sensors":651,"equipment":642},"dlio2023","DLIO","3D mechanical LiDAR (tested: Ouster OS1 with 32 channels at 10 Hz on UCLA data and the Ouster LiDAR of Newer College; Velodyne is named only as an example input) | 6-axis IMU (tested: InvenSense MPU-6050 on UCLA data; Ouster internal IMU at 100 Hz on Newer College)",{"id":653,"shortName":654,"year":655,"sensors":656,"equipment":535},"iglio2024","iG-LIO",2024,"3D LiDAR (mechanical and solid-state) | IMU",{"id":658,"shortName":659,"year":545,"sensors":660,"equipment":661},"choi2015robustrecon","Robust Reconstruction of Indoor Scenes (Redwood)","RGB-D video from a consumer depth camera (SUN3D real scenes; augmented ICL-NUIM synthetic sequences with a disparity-based noise and distortion model); fragment-pair registration is geometric (FPFH on fragment point sets), sensor models not named","compute",{"id":663,"shortName":664,"year":665,"sensors":666,"equipment":7},"cole_newman2006_3dslam","Cole-Newman 3D laser SLAM with an oscillating 2D scanner",2006,"custom 3D laser range finder: a standard 2D SICK scanner oscillating at 0.6 Hz about a horizontal axis (model not reported) | wheel odometry",{"id":668,"shortName":669,"year":521,"sensors":670,"equipment":671},"maplab2_2023","maplab 2.0","3D LiDAR | IMU (optional; framework does not require an IMU) | multi-camera or monocular camera | GNSS (RTK, optional absolute constraints) | wheel encoders and RGB-D landmarks (supported interfaces, not evaluated)","camera | imu | lidar | mobile_scanner_device | gnss | compute",{"id":673,"shortName":674,"year":599,"sensors":675,"equipment":661},"deepfactors2020","DeepFactors","monocular camera",{"id":677,"shortName":678,"year":679,"sensors":680,"equipment":681},"bundlefusion2017","BundleFusion",2017,"RGB-D","rgbd | platform | compute | other",{"id":683,"shortName":684,"year":615,"sensors":685,"equipment":686},"monoslam2007","MonoSLAM","monocular wide-angle camera (field of view near 100 degrees, 30 Hz) | 3-axis gyro fused as an internal angular-velocity measurement in the HRP-2 humanoid experiment only","camera | imu | platform | compute | other",{"id":688,"shortName":689,"year":498,"sensors":690,"equipment":691},"deng2026_mcgs_slam","MCGS SLAM","RGB-D camera (synchronized RGB-D input) | optional accelerometer and gyroscope for the motion prior; a motion-only mode infers velocity and acceleration from pose history when inertial data are absent","compute | rgbd | other",{"id":693,"shortName":694,"year":521,"sensors":695,"equipment":696},"nerfloam2023","NeRF-LOAM","3D LiDAR (MaiCity: noise-free synthetic 64-beam scans; Newer College: hand-carried LiDAR; KITTI; sensor models not named in the paper)","compute | lidar",{"id":698,"shortName":699,"year":527,"sensors":700,"equipment":701},"imlsslam2018","IMLS-SLAM","3D spinning LiDAR only (Velodyne HDL32 and HDL64 in the experiments); no IMU, GPS or camera","lidar | platform | gnss | compute",{"id":703,"shortName":704,"year":527,"sensors":705,"equipment":642},"droeschel2018ctslam","MRS continuous-time surfel SLAM (Droeschel and Behnke)","3D rotating multi-beam LiDAR (Velodyne VLP-16; two VLP-16 on the Deutsches Museum backpack) | optional IMU or wheel odometry used as registration prior and for motion during acquisition (Sec. III); the MAV carried an IMU measuring attitude (Sec. V-A)",{"id":707,"shortName":708,"year":655,"sensors":709,"equipment":710},"dufomap2024","DUFOMap","3D LiDAR | terrestrial laser scanner (survey data, qualitative)","lidar | tls_scanner | compute",{"id":712,"shortName":713,"year":599,"sensors":714,"equipment":715},"segmap2020","SegMap","3D LiDAR (KITTI odometry; sensor model not named in the paper) | rotating 2D SICK LMS-151 LiDAR on UGVs that also carried motor encoders and an Xsens MTI-G IMU (search and rescue experiments)","lidar | imu | wheel_or_leg_odometry | platform | gnss | compute",{"id":717,"shortName":718,"year":551,"sensors":719,"equipment":720},"ebadi2021dareslam","DARE-SLAM","3D LiDAR (Velodyne VLP-16 Puck Lite) | RGB-D camera (RealSense D435, object detection only)","lidar | rgbd | platform | compute | wheel_or_leg_odometry | other",{"id":722,"shortName":723,"year":492,"sensors":724,"equipment":725},"eckenhoff2019closedform","Closed-form preintegration","IMU | stereo camera (both real-world systems)","imu | stereo_camera | compute",{"id":727,"shortName":728,"year":533,"sensors":729,"equipment":730},"dpslam2003","DP-SLAM","2D laser range finder (SICK) | wheel odometry (shaft encoders)","platform | lidar | compute",{"id":732,"shortName":733,"year":515,"sensors":734,"equipment":735},"rgbdslamv2_2014","RGBDSLAMv2","RGB-D camera (structured light: Microsoft Kinect, Asus Xtion Pro Live)","rgbd | other | platform | compute",{"id":737,"shortName":738,"year":515,"sensors":675,"equipment":739},"lsdslam2014","LSD-SLAM","camera | rgbd",{"id":741,"shortName":742,"year":527,"sensors":675,"equipment":743},"dso2018","DSO","compute | stereo_camera | camera",{"id":745,"shortName":746,"year":509,"sensors":747,"equipment":748},"faizullin2022lidarsync","LiDAR sync by GNSS-clock emulation","3D LiDAR (VLP-16) | IMU (MPU-9150)","lidar | imu | other | compute | platform",{"id":750,"shortName":751,"year":498,"sensors":752,"equipment":753},"feng2026integratedslam","Integrated LiDAR SLAM for public-building sites","3D LiDAR RS-Helios-16P (16 beams, 10 Hz, +-15 deg vertical FOV, +-2 cm ranging) | nine-axis IMU at 200 Hz (recorded; the evaluated system is LiDAR-only) | camera (recorded; model not reported; not used by the method)","lidar | imu | compute | platform",{"id":755,"shortName":756,"year":655,"sensors":757,"equipment":758},"madicp2024","MAD-ICP","3D LiDAR (all quantitative evaluation) | RGB-D point clouds shown qualitatively in the supplementary material (arXiv Fig. 10: ETH3D SLAM dataset sequences einstein_1 and sofa_1, dataset cited via the BAD SLAM paper [32])","lidar | tls_scanner | gnss | compute",{"id":760,"shortName":761,"year":679,"sensors":762,"equipment":763},"forster2017preint","On-manifold IMU preintegration","monocular camera (left camera of a forward-looking VI-Sensor, 20 Hz) | IMU (ADIS16448 MEMS inside the VI-Sensor, 800 Hz)","imu | camera | other | compute",{"id":765,"shortName":766,"year":679,"sensors":767,"equipment":768},"svo2017","SVO","monocular camera | multi-camera | fisheye","stereo_camera | total_station | rgbd | other | camera | platform | compute",{"id":770,"shortName":771,"year":509,"sensors":772,"equipment":773},"artslam2022","ART-SLAM","3D LiDAR point clouds (mandatory) | optional IMU for de-skewing in the pre-filterer and orientation constraints in the pose graph | optional GPS constraints and pre-computed odometry (Sec. II-A)","lidar | imu | gnss | compute",{"id":775,"shortName":776,"year":777,"sensors":778,"equipment":779},"furgale2013unifiedcalib","Kalibr (unified temporal-spatial calibration)",2013,"global-shutter cameras (Aptina MT9V034 image sensors in a custom visual-inertial sensor) | IMU (Analog Devices ADIS16488, tactical grade)","camera | imu | other | compute",{"id":781,"shortName":782,"year":545,"sensors":783,"equipment":784},"furgale2015ct","Temporal basis functions (continuous-time batch)","stereo camera (Point Grey Research Bumblebee XB3, 24 cm baseline) for camera-IMU calibration | IMU (MicroStrain 3DM-GX2) | rolling-shutter camera (Matrix Vision BlueCougar-X102d) | global-shutter camera (Matrix Vision BlueCougar-X012b) used for comparison","stereo_camera | imu | other | camera | compute",{"id":786,"shortName":787,"year":527,"sensors":675,"equipment":743},"ldso2018","LDSO",{"id":789,"shortName":790,"year":492,"sensors":791,"equipment":792},"gawel2019fabricatorloc","In situ Fabricator BIM-referenced localization","3D LiDAR (Velodyne VLP-16) | IMU (Xsens MTi-100) | wheel encoders | three orthogonal laser distance sensors on the end-effector","lidar | imu | wheel_or_leg_odometry | other | platform | compute | total_station",{"id":794,"shortName":795,"year":796,"sensors":797,"equipment":798},"stereoscan2011","StereoScan (LIBVISO2)",2011,"stereo camera (calibrated, rectified)","stereo_camera | gnss | compute",{"id":800,"shortName":801,"year":527,"sensors":802,"equipment":803},"lips2018","LIPS","3D LiDAR (8-beam Quanergy M8 in the real test; simulator modelled on it) | IMU (Microstrain 3DM-GX3-25 in the real test; ADIS16448 model in simulation)","lidar | imu",{"id":805,"shortName":806,"year":599,"sensors":807,"equipment":808},"openvins2020","OpenVINS","monocular or stereo camera (arbitrary number of cameras supported) | IMU","stereo_camera | imu | compute",{"id":810,"shortName":811,"year":615,"sensors":812,"equipment":813},"glennie2007rigorous","Kinematic LiDAR error budget","LiDAR | GNSS | IMU","lidar | imu | other | gnss",{"id":815,"shortName":816,"year":579,"sensors":817,"equipment":818},"glennie2012hdl64","HDL-64E S2 calibration","3D LiDAR (Velodyne HDL-64E S2) | IMU (IMAR AirSurv-RQH, navigation grade) | GNSS (Novatel OEM-4 dual-frequency GPS receiver)","lidar | imu | gnss | platform",{"id":820,"shortName":821,"year":527,"sensors":822,"equipment":823},"limo2018","LIMO","monocular camera (KITTI grayscale images used for feature tracking; camera model not named in the paper) | 3D LiDAR (KITTI; model not named in the paper), used only to give depth to image features","lidar | camera | compute",{"id":825,"shortName":826,"year":615,"sensors":827,"equipment":730},"gmapping2007","GMapping","2D laser range finder | wheel odometry",{"id":829,"shortName":830,"year":539,"sensors":831,"equipment":832},"kinematicicp2025","Kinematic-ICP","3D LiDAR (Robosense Bpearl, Hesai XT32) | wheel odometry","platform | lidar | wheel_or_leg_odometry | total_station | compute",{"id":834,"shortName":835,"year":836,"sensors":837,"equipment":838},"gutmann_konolige1999_lrgc","LRGC (Local Registration and Global Correlation)",1999,"2D laser range finder (180 deg SICK) | wheel odometry","platform | lidar",{"id":840,"shortName":841,"year":655,"sensors":842,"equipment":661},"gsicpslam2024","GS-ICP SLAM","RGB-D camera",{"id":844,"shortName":845,"year":527,"sensors":846,"equipment":847},"flashfusion2018","FlashFusion","RGB-D camera (Asus Xtion for live scanning; TUM RGB-D real sequences; synthetic noisy ICL-NUIM)","rgbd | compute",{"id":849,"shortName":850,"year":655,"sensors":851,"equipment":852},"hatleskog2024probdegen","Probabilistic degeneracy detection (DRPM)","3D LiDAR (Velodyne VLP-16 in exp. 1-2; Ouster OS0-64 RevD in exp. 3; Ouster OS0-128 RevD in exp. 4) | IMU (Alphasense IMU in exp. 2; PixRacer Pro autopilot IMU in exp. 3; VectorNav VN100 in exp. 4) | monochrome camera for the ROVIO visual-inertial prior (exp. 2) | radar (TI IWR6843AOP-EVM) for a velocity factor (exp. 4 only) | legged odometry (exp. 1)","lidar | platform | camera | imu | radar | compute",{"id":854,"shortName":855,"year":521,"sensors":856,"equipment":857},"pointlio2023","Point-LIO","Livox Avia solid-state LiDAR with built-in BMI088 IMU (own experiments) | public benchmarks with Livox Horizon, Velodyne HDL-32E and VLP-16 plus their IMUs","lidar | imu | camera | other | platform | compute",{"id":859,"shortName":860,"year":521,"sensors":861,"equipment":862},"he2023ikfom","IKFoM","3D LiDAR (Livox AVIA solid-state LiDAR) | IMU (built into the Livox AVIA) | spinning multiline LiDAR and IMU data of the public LIO-SAM Campus sequences for the LINS re-implementation (sensor models not stated)","lidar | imu | compute | other | platform",{"id":864,"shortName":865,"year":551,"sensors":866,"equipment":867},"hendrikx2021semanticbimloc","Semantic BIM for 2D LiDAR localization","2D LiDAR (Hokuyo UTM30-LX, mounted upside down near the floor, 180 deg FOV, 720 points) | wheel encoder odometry","lidar | platform | wheel_or_leg_odometry",{"id":869,"shortName":870,"year":579,"sensors":871,"equipment":872},"rgbdmapping2012","RGB-D Mapping (Henry et al.)","RGB-D camera (PrimeSense active-stereo camera equivalent to the Kinect sensor, 640 x 480 registered image and depth at 30 fps; Sec. 1)","rgbd | lidar | other",{"id":874,"shortName":875,"year":876,"sensors":877,"equipment":878},"cartographer2016","Cartographer",2016,"horizontally mounted 2D LIDAR on the backpack (model not reported) | IMU on the backpack (model not reported) | Neato Robotics Revo LDS low-cost laser distance sensor (second experiment)","lidar | imu | platform | compute | other",{"id":880,"shortName":881,"year":492,"sensors":882,"equipment":883},"hinduja2019degeneracy","Degeneracy-aware factors","multibeam imaging sonar (DIDSON, 96 beams, profiling mode with a concentrator lens giving 1 degree vertical FOV) | DVL (Teledyne\u002FRDI Workhorse Navigator, 1.2 MHz) | AHRS with Honeywell HG1700 IMU | depth sensor (Paroscientific Digiquartz)","platform | other | imu",{"id":885,"shortName":886,"year":572,"sensors":887,"equipment":535},"hong2010vicp","VICP","2D laser rangefinder (Hokuyo URG-04LX, written HOKUYO URG04-LX)",{"id":889,"shortName":890,"year":655,"sensors":891,"equipment":892},"livgaussmap2024","LIV-GaussMap","3D LiDAR (Livox Avia; Ouster OS1-128; solid-state RealSense L515) | IMU | monocular camera (global shutter; rolling shutter on the L515)","lidar | imu | camera | other | compute",{"id":894,"shortName":895,"year":539,"sensors":896,"equipment":897},"gslivo2025","GS-LIVO","3D LiDAR | IMU | camera","lidar | imu | camera | other | platform | compute | gnss | tls_scanner",{"id":899,"shortName":900,"year":777,"sensors":901,"equipment":902},"hornung2013octomap","OctoMap","[\"2D laser range finder on a pan-tilt unit (SICK LMS, FR-079 corridor)\", \"two fixed laser scanners sweeping to the left and right of the robot (New College Epoch C | models not stated)\", \"dense 3D laser scans (Freiburg campus, 81 scans | sensor model not stated | ranges up to 50 m)\", \"RGB-D camera (Microsoft Kinect, freiburg1_360 sequence)\"]","lidar | platform | rgbd | compute",{"id":904,"shortName":905,"year":679,"sensors":906,"equipment":907},"fovis2017","FOVIS","RGB-D camera: stripped-down Microsoft Kinect (PrimeSense), 640 x 480 RGB-D at 30 Hz | IMU on the vehicle, fused with the visual odometry in an EKF for control (not used inside the visual odometry)","rgbd | imu | platform | compute | other",{"id":909,"shortName":910,"year":655,"sensors":911,"equipment":661},"huang2024_2dgs","2DGS","monocular camera (multi-view images)",{"id":913,"shortName":914,"year":655,"sensors":915,"equipment":916},"loglio2024","LOG-LIO","3D spinning LiDAR with ring index (Velodyne 32-beam in M2DGR; Ouster OS1 16-channel in NTU VIRAL) | 9-axis IMU (VectorNav VN100 in NTU VIRAL)","lidar | platform | other | imu | total_station | compute",{"id":918,"shortName":919,"year":655,"sensors":920,"equipment":921},"photoslam2024","Photo-SLAM","monocular camera | stereo camera (EuRoC MAV dataset; hand-held ZED 2 outdoors) | RGB-D","stereo_camera | compute",{"id":923,"shortName":924,"year":533,"sensors":925,"equipment":926},"hahnel2003_gridfastslam","Grid-based FastSLAM with scan matching","2D laser range finder (SICK LMS) | wheel odometry","platform | lidar | compute | other",{"id":928,"shortName":929,"year":533,"sensors":930,"equipment":931},"hahnel2003_compact3d","Compact 3D building models from mobile laser scanning","2D laser range finders: indoors a horizontal laser for 2D mapping plus an upward-pointing laser for 3D (SICK PLS used in the Wean Hall run; SICK LMS also named); outdoors one laser on a pan\u002Ftilt unit | odometry","lidar | platform",{"id":933,"shortName":934,"year":521,"sensors":680,"equipment":661},"eslam2023","ESLAM",{"id":936,"shortName":937,"year":521,"sensors":938,"equipment":939},"malio2023","MA-LIO","multiple asynchronous 3D LiDARs of different makes and scan patterns (Ouster OS0-64 plus Livox; Velodyne HDL-32E, VLP-16 and LS-16C; Ouster OS2-128 plus Livox Avia and Livox Tele) | IMU (100 to 400 Hz; models not stated)","lidar | imu | gnss | platform | compute",{"id":941,"shortName":942,"year":509,"sensors":943,"equipment":944},"kayhani2022tagvio","Tag-based VIO for indoor construction UAVs","forward-looking monocular camera of the Parrot Bebop 2 (rectified 856 x 480 at about 30 Hz) | onboard IMU and odometry velocities of the Bebop 2 (about 5 Hz, upsampled to the image rate)","platform | camera | imu | other",{"id":946,"shortName":947,"year":655,"sensors":680,"equipment":948},"splatam2024","SplaTAM","compute | camera | rgbd",{"id":950,"shortName":951,"year":777,"sensors":952,"equipment":735},"keller2013pointfusion","Point-based fusion","[\"RGB-D camera (Microsoft Kinect, near mode, 640x480 depth)\", \"time-of-flight camera (PMD CamBoard, 200x200, per-pixel amplitude used for confidence)\"]",{"id":954,"shortName":955,"year":521,"sensors":911,"equipment":661},"kerbl2023_3dgs","3DGS",{"id":957,"shortName":958,"year":777,"sensors":842,"equipment":959},"dvoslam2013","DVO-SLAM","rgbd | other | compute",{"id":961,"shortName":962,"year":599,"sensors":963,"equipment":964},"compslam2020","CompSLAM","3D LiDAR (Velodyne PuckLITE on the underpass UAV; Ouster OS1-64 in the mine deployment) | IMU (VectorNav VN-100) | visual camera (FLIR Blackfly with shutter-synchronized LEDs) for VIO | LWIR thermal camera (FLIR Tau2, full radiometric imagery) for TIO","platform | lidar | imu | camera | compute | thermal",{"id":966,"shortName":967,"year":527,"sensors":968,"equipment":623},"scancontext2018","Scan Context","3D LiDAR (Velodyne HDL-64E on KITTI, HDL-32E on NCLT, two tilted VLP-16 merged on Complex Urban LiDAR)",{"id":970,"shortName":971,"year":509,"sensors":972,"equipment":803},"ltmapper2022","LT-mapper","3D LiDAR | IMU (optional, for initial odometry)",{"id":974,"shortName":975,"year":615,"sensors":675,"equipment":976},"ptam2007","PTAM","camera | compute",{"id":978,"shortName":979,"year":545,"sensors":980,"equipment":981},"chisel2015","CHISEL","Google Tango 'Peanut' phone: projective depth sensor (6 Hz), 120 deg wide-angle tracking camera (60 Hz), 4 MP colour camera (30 Hz), six-axis gyroscope and accelerometer | Google Tango 'Yellowstone' tablet: projective depth sensor (3 Hz), same tracking camera, 4 MP colour camera (30 Hz) | Kinect RGB-D data of the Freiburg (TUM) benchmark for memory experiments","mobile_scanner_device | compute | rgbd | other",{"id":983,"shortName":984,"year":796,"sensors":985,"equipment":986},"hector2011","Hector SLAM","2D laser range finder | IMU","lidar | compute | imu | mobile_scanner_device | platform",{"id":988,"shortName":989,"year":551,"sensors":990,"equipment":991},"interactiveslam2021","interactive_slam","3D LiDAR (16-line in the indoor-outdoor sequence; model not reported) | input is any ROS SLAM pose graph or odometry sequence","total_station | mobile_scanner_device",{"id":993,"shortName":994,"year":551,"sensors":995,"equipment":535},"koide2021vgicp","VGICP","3D LiDAR (Velodyne HDL-32E real and simulated)",{"id":997,"shortName":998,"year":655,"sensors":999,"equipment":1000},"glim2024","GLIM","3D LiDAR (spinning and non-repetitive) | depth cameras (ToF, active stereo, stereo) | IMU | optional multi-camera","lidar | camera | rgbd | stereo_camera | tls_scanner | imu | uwb | compute",{"id":1002,"shortName":1003,"year":572,"sensors":1004,"equipment":661},"karto_spa2010","Karto SLAM (Sparse Pose Adjustment)","front-end agnostic optimizer; test graphs were built by the Karto front end from 2D laser logs (laser model not reported)",{"id":1006,"shortName":1007,"year":545,"sensors":1008,"equipment":1009},"infinitam2015","InfiniTAM","Depth camera: Microsoft Kinect for XBOX 360 (teddy sequence, 640x480 colour and disparity) | Depth camera: Occipital Structure Sensor (couch sequence, 320x240 depth) | IMU of the tablet (Apple iPad Air 2 orientation for the couch sequence; tablet IMU in the swivel-chair test)","rgbd | imu | mobile_scanner_device | compute | other",{"id":1011,"shortName":1012,"year":492,"sensors":1013,"equipment":1014},"rtabmap2019","RTAB-Map","RGB-D | stereo | 2D LiDAR | 3D LiDAR | wheel odometry | IMU only through external odometry (wheel and IMU EKF) or integrated visual-inertial odometry such as OKVIS, MSCKF and Google Tango (Sec. 3.1.1, 4.3, 4.4) | the bag-of-words loop closure needs a camera, and without one the authors suggest feeding an empty image and relying on laser proximity detection only (Sec. 6)","compute | stereo_camera | lidar | platform | rgbd | imu | wheel_or_leg_odometry",{"id":1016,"shortName":1017,"year":492,"sensors":1018,"equipment":1019},"laconte2019lidarbias","Incidence-angle LiDAR bias model","2D LiDAR (SICK LMS151) | 3D LiDAR (Velodyne HDL-32E, Robosense RS-LiDAR-16)","lidar | other",{"id":1021,"shortName":1022,"year":521,"sensors":1023,"equipment":1024},"cocolic2023","Coco-LIC","3D LiDAR (Velodyne VLP-16, HDL-32E, Livox Avia across datasets) | IMU | monocular camera","lidar | imu | other | camera | stereo_camera | platform | compute",{"id":1026,"shortName":1027,"year":539,"sensors":499,"equipment":1028},"gaussianlic2025","Gaussian-LIC","compute | lidar | camera | imu",{"id":1030,"shortName":1031,"year":527,"sensors":1032,"equipment":1033},"legentil2018lidarimucalib","Lidar-IMU calibration with upsampled preintegration","3D LiDAR (VLP-16) | IMU (Xsens MTi-3)","lidar | imu | rgbd",{"id":1035,"shortName":1036,"year":599,"sensors":1037,"equipment":642},"legentil2020gpm","Gaussian Process Preintegration (GPM)","IMU | 3D LiDAR (validation within IN2LAAMA)",{"id":1039,"shortName":1040,"year":551,"sensors":1041,"equipment":1042},"in2laama2021","IN2LAAMA","3D spinning LiDAR with per-point timestamps (Velodyne VLP-16; Velodyne HDL-32 in the MC2SLAM campus drive sequence) | 6-DoF IMU (Xsens MTi-3 at 100 Hz; HDL-32 built-in IMU)","lidar | imu | platform | camera",{"id":1044,"shortName":1045,"year":539,"sensors":1046,"equipment":1047},"mins2025","MINS","IMU | cameras (one or more) | wheel encoders | LiDAR (one or more) | GNSS","imu | stereo_camera | lidar | gnss | wheel_or_leg_odometry | platform | other | compute",{"id":1049,"shortName":1050,"year":655,"sensors":1051,"equipment":976},"mast3r2024","MASt3R","monocular camera (image pairs)",{"id":1053,"shortName":1054,"year":545,"sensors":1055,"equipment":1056},"okvis2015","OKVIS","stereo | monocular camera | IMU","imu | stereo_camera | other | compute | gnss | platform",{"id":1058,"shortName":1059,"year":777,"sensors":546,"equipment":1060},"msckf2_2013","MSCKF 2.0","imu | stereo_camera | gnss | platform | compute",{"id":1062,"shortName":1063,"year":515,"sensors":1064,"equipment":1065},"li2014onlinetemporal","Online temporal calibration (camera-IMU)","monocular camera (one camera of a PointGrey Bumblebee2 stereo pair, 20 Hz) | IMU (Xsens MTI-G, 100 Hz)","stereo_camera | imu | gnss | other",{"id":1067,"shortName":1068,"year":492,"sensors":1069,"equipment":1070},"lonet2019","LO-Net","Velodyne HDL-64 3D LiDAR (KITTI and Ford data; Sec. 4, 4.5)","lidar | gnss | imu | compute",{"id":1072,"shortName":1073,"year":551,"sensors":1074,"equipment":535},"saloam2021","SA-LOAM","3D LiDAR only (Velodyne HDL-64E in KITTI and Ford Campus); per-point semantics from a pre-trained RangeNet++ network (Sec. IV-A)",{"id":390,"shortName":391,"year":551,"sensors":1076,"equipment":642},"solid-state LiDAR (Livox Horizon, 81.7 x 25.1 deg FoV) or spinning LiDAR (HDL-32E, HDL-64E) | IMU (6-axis sufficient; Xsens MTi-670 in own suite)",{"id":1078,"shortName":1079,"year":615,"sensors":1080,"equipment":1081},"lichti2007amcw","AM-CW TLS error model and self-calibration","terrestrial laser scanner Faro 880 (AM-CW phase-difference rangefinder; formerly iQsun 880) | integrated dual-axis inclinometers of the Faro 880 | total station and 900 mm Leica scale bar (independent check-point survey only)","tls_scanner | other | total_station",{"id":1083,"shortName":1084,"year":521,"sensors":1085,"equipment":1086},"adalio2023","AdaLIO","3D LiDAR | IMU (HILTI-Oxford dataset sensors; models not named in the paper)","platform",{"id":1088,"shortName":1089,"year":655,"sensors":1090,"equipment":1091},"lim2024quatropp","Quatro++","3D LiDAR (Velodyne HDL-64E, VLP-16, Ouster OS1-64, HESAI XT32 across datasets) | INS optional for roll\u002Fpitch (Sec. 5.5)","lidar | imu | compute",{"id":1093,"shortName":1094,"year":539,"sensors":1095,"equipment":535},"lim2025kissmatcher","KISS-Matcher","64-channel 3D LiDAR (KITTI and MulRan, different ray patterns; models not named in the paper) | map clouds produced by FAST-LIO2-based SLAM on the Kimera-Multi dataset (sensors not stated in the paper)",{"id":387,"shortName":388,"year":599,"sensors":1097,"equipment":1098},"solid-state LiDAR (Livox Mid-40, 38.4 deg circular FoV, non-repetitive scan)","lidar | camera | platform | other | gnss | compute",{"id":1100,"shortName":1101,"year":509,"sensors":1102,"equipment":1103},"r3live2022","R3LIVE","3D LiDAR (Livox AVIA, FoV 70.4 x 77.2 deg) | IMU (labelled 'Build-in IMU' of the LiDAR in the device figure; model not reported) | global-shutter RGB camera (FLIR Blackfly BFS-u3-13y3c, FoV 82.9 x 66.5 deg) | D-GPS RTK system (reference only) | ArUco marker board (drift reference in GPS-denied tests)","lidar | imu | camera | compute | gnss | other | platform",{"id":1105,"shortName":1106,"year":655,"sensors":1107,"equipment":1108},"r3livepp2024","R3LIVE++","3D LiDAR (LiVOX AVIA in the R3LIVE-dataset; NCLT 3D LiDAR, model not named in the paper) | IMU (model not reported) | RGB camera (FLIR Blackfly BFS-u3-13y3c global shutter in the R3LIVE-dataset; front-facing camera of the NCLT omnidirectional camera)","lidar | camera | compute | other | platform | gnss | wheel_or_leg_odometry",{"id":1110,"shortName":1111,"year":551,"sensors":1112,"equipment":1113},"r2live2021","R2LIVE","3D LiDAR (Livox AVIA, FoV 70.4 x 77.2 deg) | IMU (model not reported; 200 Hz in the Fig. 3 illustration) | monocular global-shutter camera (FLIR Blackfly BFS-u3-13y3c, FoV 82.9 x 66.5 deg; model named only in the version of record) | D-GPS RTK system (reference only; footnote links the DJI D-RTK product page)","lidar | camera | imu | compute | gnss | platform",{"id":1115,"shortName":1116,"year":521,"sensors":1117,"equipment":1118},"lin2023immesh","ImMesh","3D LiDAR (spinning and solid-state) | IMU (optional)","platform | lidar | compute | other | camera",{"id":1120,"shortName":1121,"year":655,"sensors":675,"equipment":661},"dpvslam2024","DPV-SLAM",{"id":1123,"shortName":1124,"year":655,"sensors":680,"equipment":1125},"loopyslam2024","Loopy-SLAM","compute | other | rgbd",{"id":1127,"shortName":1128,"year":521,"sensors":1129,"equipment":1130},"balm2_2023","BALM2 (BALM 2.0)","3D LiDAR","lidar | total_station | other | gnss | platform | compute",{"id":1132,"shortName":1133,"year":521,"sensors":1134,"equipment":535},"hba2023","HBA","3D LiDAR (mechanical spinning in the public datasets; solid-state LiDAR of ref. [26], retina-like incommensurable scanning, in the self-collected data)",{"id":1136,"shortName":1137,"year":655,"sensors":1138,"equipment":1139},"glio2024","GLIO","low-cost GNSS receiver raw pseudorange and Doppler (u-blox F9P) with reference-station corrections | IMU (Xsens Ti-10) | 3D LiDAR (Velodyne HDL-32E)","gnss | imu | lidar",{"id":1141,"shortName":1142,"year":539,"sensors":1143,"equipment":661},"slam3r2025","SLAM3R","monocular RGB camera (video)",{"id":1145,"shortName":1146,"year":498,"sensors":1147,"equipment":1148},"voxelslam2026","Voxel-SLAM","3D LiDAR | IMU","lidar | imu | mobile_scanner_device | compute",{"id":1150,"shortName":1151,"year":1152,"sensors":827,"equipment":931},"lu_milios1997","Lu-Milios global scan alignment",1997,{"id":1154,"shortName":1155,"year":579,"sensors":1156,"equipment":1157},"lupton2012preint","Lupton-Sukkarieh preintegration","IMU (Honeywell HG1900, 600 Hz) | stereo camera (Point Grey Research Bumblebee2 with 2.1 mm wide-angle lenses, 6.25 Hz, 12 cm baseline) | monocular camera (left camera of the stereo pair only, Sec. VIII-G)","imu | stereo_camera | platform",{"id":1159,"shortName":1160,"year":599,"sensors":1161,"equipment":1162},"lv2020licalib","LI-Calib","3D LiDAR (VLP-16) | IMU (three Xsens MTi-100 series)","lidar | imu | other | platform",{"id":1164,"shortName":1165,"year":551,"sensors":1166,"equipment":818},"clins2021","CLINS","3D spinning LiDAR (Velodyne VLP-16 in the YQ and KAIST Urban tests) | IMU (Xsens MTi-300)",{"id":1168,"shortName":1169,"year":521,"sensors":1170,"equipment":1171},"clic2023","CLIC","one or two 3D LiDARs | IMU | monocular camera (optional)","lidar | imu | camera | gnss | other | compute",{"id":1173,"shortName":1174,"year":551,"sensors":1175,"equipment":696},"slamtoolbox2021","SLAM Toolbox","laser scanner (paper); planar 2D scans per the repository README, which the paper itself does not state | wheel or other odometry supplied as the odom-to-base transform (repository README; not stated in the paper)",{"id":1177,"shortName":1178,"year":539,"sensors":1179,"equipment":1180},"vggtslam2025","VGGT-SLAM","monocular camera (uncalibrated)","compute | camera",{"id":1182,"shortName":1183,"year":615,"sensors":1184,"equipment":1185},"magnusson2007ndt3d","3D-NDT","Optab Optronikinnovation AB prototype 3D laser range finder (modulated infrared laser on a rotating mirror, phase-shift ranging; pitching scans for TUNNEL, yawing scans for JUNCTION) | SICK LMS 200 2D laser scanner on a pan-tilt unit giving pitching 3D scans of about 95,000 points (KVARNTORP-LOOP) | 2D wheel odometry of the robot for initial pose estimates (KVARNTORP-LOOP) | total station measuring three marked points on the scanner (TUNNEL); not accurate enough as ground truth, used only as initial estimate","lidar | platform | wheel_or_leg_odometry | total_station | compute",{"id":1187,"shortName":1188,"year":575,"sensors":1189,"equipment":1190},"magnusson2009thesis","3D-NDT thesis","SICK 2D lidar on a pan\u002Ftilt unit producing pitching 3D scans on Tjorven (180 deg horizontal, about 100 deg vertical field of view; SICK model not named for Tjorven) | SICK lidar on a continuously rotating slip-ring mount (yawing omnidirectional scans) and a Hokuyo 2D lidar for 2D localisation on Alfred | tiltable SICK laser scanner on Kurt3D (pitching scans) | PMD[vision] 19k time-of-flight camera combined with a Matrix-Vision Blue Fox colour camera (Colour-NDT data) | SwissRanger time-of-flight camera (3D-Cam scan pair, collected by Jacobs University Bremen) | SICK lidar on a Schunk PowerCube via slip-ring contacts with a digital camera (Kemi mine muck-pile scans) | simulated yawing lidar (Sci-Fi and Sim-Mine scan pairs) | wheel-encoder odometry for initial pose estimates (Kvarntorp-Loop, Mission-4)","platform | lidar | other | camera | wheel_or_leg_odometry | compute",{"id":1192,"shortName":1193,"year":498,"sensors":1194,"equipment":1195},"rkolio2026","RKO-LIO","3D LiDAR | IMU (consumer to industrial grade)","lidar | imu | tls_scanner | platform | gnss | other",{"id":1197,"shortName":1198,"year":655,"sensors":1199,"equipment":1200},"monogs2024","MonoGS (Gaussian Splatting SLAM)","monocular camera | RGB-D | stereo (depth from stereo, tested only on EuRoC Machine Hall in Supp. 9.5)","rgbd | compute | stereo_camera",{"id":1202,"shortName":1203,"year":599,"sensors":911,"equipment":976},"nerf2020","NeRF",{"id":1205,"shortName":1206,"year":527,"sensors":1207,"equipment":1208},"cblox2018","C-blox","Stereo camera pair of a VI-sensor (global shutter, tightly synchronized) for ORB-SLAM2 tracking on the MAV | RGB-D camera Intel RealSense D415 (coloured pointclouds) for dense integration on the MAV | Synthetic RGB-D input (ICL-NUIM) and simulated RGB plus noiseless depth (CARLA) in the evaluations","platform | stereo_camera | rgbd | compute",{"id":1210,"shortName":1211,"year":655,"sensors":1212,"equipment":1213},"millane2024nvblox","nvblox","RGB-D | 3D LiDAR","rgbd | lidar | compute",{"id":1215,"shortName":1216,"year":1217,"sensors":1218,"equipment":838},"fastslam2002","FastSLAM",2002,"[\"2D laser range finder (SICK)\",\"robot controls u_t (odometry sensor not named in the paper)\"]",{"id":1220,"shortName":1221,"year":796,"sensors":1222,"equipment":1223},"velodyneslam2011","Velodyne SLAM","3D spinning LiDAR only (Velodyne HDL-64E S2); no wheel-speed, inertial or other information","lidar | platform | gnss",{"id":1225,"shortName":1226,"year":551,"sensors":1227,"equipment":1228},"moura2021bimslam","BIM-based localization and mapping (COBOLLEAGUE)","3D LiDAR (model not reported) | odometry encoders | IMU","lidar | wheel_or_leg_odometry | imu",{"id":1230,"shortName":1231,"year":615,"sensors":546,"equipment":1232},"mourikis2007msckf","MSCKF","camera | imu | platform | compute",{"id":402,"shortName":403,"year":679,"sensors":1234,"equipment":1235},"monocular camera | stereo | RGB-D","compute | stereo_camera | platform | rgbd",{"id":1237,"shortName":1238,"year":545,"sensors":675,"equipment":1239},"orbslam2015","ORB-SLAM","compute | stereo_camera | platform | rgbd | other | camera | gnss | lidar",{"id":1241,"shortName":1242,"year":539,"sensors":1243,"equipment":1244},"mast3rslam2025","MASt3R-SLAM","monocular camera (uncalibrated, generic central camera)","compute | rgbd | other | camera",{"id":1246,"shortName":1247,"year":492,"sensors":1248,"equipment":1249},"mc2slam2019","MC2SLAM","3D multi-beam LiDAR (Velodyne HDL-32 with built-in IMU in the authors' data; Velodyne HDL-64 in KITTI without IMU) | IMU (built-in HDL-32 IMU)","lidar | imu | platform | gnss | compute",{"id":1251,"shortName":1252,"year":796,"sensors":675,"equipment":976},"dtam2011","DTAM",{"id":399,"shortName":400,"year":796,"sensors":680,"equipment":959},{"id":1255,"shortName":1256,"year":509,"sensors":1257,"equipment":1258},"viralfusion2022","VIRAL-Fusion","[\"IMU (VectorNav VN-100, per footnote link)\", \"body-offset UWB ranging (Humatics P440, per footnote link | two UAV nodes with two antennae each and three or four anchors)\", \"camera system (footnote links the ueye_cam driver | used through VINS-Fusion odometry)\", \"two LiDARs (Ouster OS1, per footnote link | horizontal and vertical, used through A-LOAM odometry)\"]","imu | uwb | lidar | camera | total_station | other",{"id":1260,"shortName":1261,"year":521,"sensors":1262,"equipment":1263},"slict2023","SLICT","one or several 3D LiDARs merged into one stream (Ouster OS1-128 plus prism-based Livox Mid-70 in-house; horizontal and vertical LiDARs in NTU VIRAL; Ouster 64-channel in Newer College) | IMU (VectorNav VN100 in-house; built-in 100 Hz Ouster IMU in Newer College)","lidar | imu | platform | tls_scanner | total_station | compute",{"id":1265,"shortName":1266,"year":777,"sensors":680,"equipment":847},"voxelhashing2013","Voxel Hashing",{"id":1268,"shortName":1269,"year":1270,"sensors":1271,"equipment":1272},"nister2004vo","Visual Odometry (Nistér et al.)",2004,"stereo camera (calibrated) | monocular camera","stereo_camera | gnss | imu | platform | compute",{"id":1274,"shortName":1275,"year":551,"sensors":1276,"equipment":623},"nubert2021delora","DeLORA","Velodyne VLP-16 Puck Lite (ANYmal, Sec. IV-A) | Ouster OS1-64 (DARPA SubT Urban, Sec. IV-B) | KITTI odometry LiDAR (sensor model not named in the paper, Sec. IV-C)",{"id":1278,"shortName":1279,"year":509,"sensors":1280,"equipment":1281},"nubert2022learninglocalizability","Learning-based localizability","3D LiDAR (Velodyne VLP-16; Ouster OS0-128 for sensor-transfer test)","lidar | platform | tls_scanner | mobile_scanner_device | compute",{"id":1283,"shortName":1284,"year":498,"sensors":1285,"equipment":1286},"holisticfusion2026","Holistic Fusion","IMU (core) | LiDAR registration poses (e.g., Open3D SLAM, CompSLAM, Coin-LIO outputs) | GNSS (single or dual antenna) | leg kinematics or wheel encoder | mm-wave radar velocity | cabin rotary encoder (HEAP)","platform | lidar | other | compute",{"id":1288,"shortName":1289,"year":615,"sensors":1290,"equipment":1291},"nuchter2007_6dslam","6D SLAM (Kurt3D, stop-scan-go ICP SLAM)","3D laser range finder built from a SICK 2D scanner on a servo-driven pitch mount (Sec. 5.1) | wheel odometry used only for initial pose extrapolation (Sec. 3.1)","lidar | platform | wheel_or_leg_odometry | compute | other",{"id":1293,"shortName":1294,"year":551,"sensors":1295,"equipment":1296},"rloam2021","R-LOAM","Simulated Velodyne VLP-16 (16 lines, up to 30,000 points per scan, 10 Hz, Gaussian noise sigma 0.03) and simulated Ouster OS1-128 (128 lines, up to 262,144 points per scan, 10 Hz, sigma 0.05), one at a time, on a 1-DoF gimbal tilting between -0.6 and +0.6 rad on a quadcopter UAV in Gazebo (Sec. IV-A, Fig. 4)","lidar | other | platform",{"id":1298,"shortName":1299,"year":509,"sensors":1300,"equipment":1301},"roloam2022","RO-LOAM","Velodyne VLP-16 (10 Hz) on a Dynamixel actuator rotating the LiDAR about the roll axis within +\u002F-40 deg | actuator readings transform scans to the robot frame | Intel NUC logs data to SSD (Sec. III-A, IV-A)","lidar | other | platform | compute | total_station | tls_scanner",{"id":1303,"shortName":1304,"year":679,"sensors":1305,"equipment":1306},"oleynikova2017voxblox","Voxblox","RGB-D | stereo","rgbd | other | total_station | stereo_camera | compute | platform | imu",{"id":1308,"shortName":1309,"year":492,"sensors":1310,"equipment":1311},"refusion2019","ReFusion","RGB-D camera (ASUS Xtion Pro LIVE in the Bonn RGB-D Dynamic Dataset; TUM RGB-D dynamic sequences)","rgbd | other | tls_scanner",{"id":1313,"shortName":1314,"year":551,"sensors":1315,"equipment":1316},"locus2021","LOCUS","one or more 360-degree 3D LiDARs (two Velodyne VLP16 on Husky, one flat and one pitched forward 30 deg; one VLP16 on Spot) | IMU (Vector Nav 100 on Husky), used for rotation priors and motion distortion correction | external odometry used loosely: wheel-inertial (WIO), visual-inertial (VIO) and kinematic-inertial (KIO) odometry","lidar | imu | wheel_or_leg_odometry | other | platform | compute",{"id":1318,"shortName":1319,"year":551,"sensors":1320,"equipment":1321},"mulls2021","MULLS","3D LiDAR (seven types: Velodyne HDL-64E, VLP-32C, HDL-32E; Hesai Pandar QT Lite, XT, 64, 128); no IMU required","lidar | gnss | platform | tls_scanner | compute",{"id":1323,"shortName":1324,"year":655,"sensors":1325,"equipment":1326},"pinslam2024","PIN-SLAM","Velodyne HDL64 (KITTI) | Ouster OS1-64 (MulRAN; Newer College long sequences; IPB-Car 2020) | OS1-128 (IPB-Car 2023) | OS0-128 (Newer College shorter sequences) | OS0-64 (Hilti-21, handheld) | 32-beam LiDAR on a Spot robot (Nebula, qualitative) | synthetic RGB-D (Replica)","lidar | platform | gnss | tls_scanner | total_station | other | compute",{"id":1328,"shortName":1329,"year":539,"sensors":1330,"equipment":1331},"pings2025","PINGS","Ouster OS1-128 LiDAR, 128 beams, 45 deg vertical FOV, 10 Hz, mounted horizontally (in-house car dataset) | four Basler Ace cameras giving 360 deg coverage at 10 Hz (in-house car dataset) | Oxford Spires handheld rig: 64-beam LiDAR and three global-shutter cameras (dataset)","lidar | camera | platform | gnss | tls_scanner | compute",{"id":1333,"shortName":1334,"year":527,"sensors":1335,"equipment":1336},"elasticlidarfusion2018","Elastic LiDAR Fusion","rotating 2D LiDAR (Hokuyo UTM-30LX spinning, with encoder) | IMU (Microstrain 3DM-GX3) | Grasshopper3 2.8 MP colour camera with fisheye lens used only for colourisation | Optris PI 450 thermal-infrared camera (382 x 288 pixels) on the device but not used","lidar | other | imu | camera | thermal | mobile_scanner_device",{"id":1338,"shortName":1339,"year":509,"sensors":1340,"equipment":1341},"elasticity_ct2022","Map-centric dense 3D LiDAR SLAM (ElasticLiDAR++)","rotating 2D LiDAR (Hokuyo UTM-30LX with encoder, rotor at 1 rotation\u002Fs, hand-held) | 3D LiDAR (Velodyne VLP-16, hand-held and robot-mounted) | IMU (Microstrain 3DM-GX3 in hand-held payloads | model not stated for the robot payload) | RGB camera on the single-beam device | independent GoPro without common clock on the multi-beam hand-held device | camera on robot payload (model not stated)","lidar | other | imu | camera | platform | mobile_scanner_device | compute",{"id":1343,"shortName":1344,"year":655,"sensors":1345,"equipment":847},"rtgslam2024","RTG-SLAM","RGB-D camera (Microsoft Azure Kinect for the self-scanned dataset)",{"id":1347,"shortName":1348,"year":655,"sensors":1349,"equipment":1350},"coinlio2024","COIN-LIO","high-resolution 3D LiDAR with intensity (Ouster OS0-128) | IMU","lidar | imu | total_station | platform | compute",{"id":1352,"shortName":1353,"year":515,"sensors":1354,"equipment":623},"pomerleau2014_icpmapper","Long-term 3D map maintenance in dynamic environments","3D LiDAR (Velodyne HDL-32E) | wheel odometry (prior alignment for registration)",{"id":1356,"shortName":1357,"year":551,"sensors":1358,"equipment":1359},"rflio2021","RF-LIO","3D LiDAR at 10 Hz (model not named) | IMU at 400 Hz (model not named)","gnss | compute | lidar | imu",{"id":1361,"shortName":1362,"year":1363,"sensors":1364,"equipment":7},"aloam_software","A-LOAM",null,"3D spinning LiDAR only: launch files for Velodyne VLP-16, HDL-32 and HDL-64 (README examples name 'Velodyne VLP-16' and 'Velodyne HDL-64'; 'HDL-64E' is not written); no IMU subscription in the code",{"id":1366,"shortName":1367,"year":527,"sensors":546,"equipment":1368},"vinsmono2018","VINS-Mono","stereo_camera | imu | other | total_station | camera | compute | platform | mobile_scanner_device",{"id":1370,"shortName":1371,"year":492,"sensors":1055,"equipment":1372},"vinsfusion2019","VINS-Fusion (local odometry framework)","stereo_camera | imu | other | total_station | gnss",{"id":1374,"shortName":1375,"year":599,"sensors":1376,"equipment":939},"lins2020","LINS","3D LiDAR (Velodyne VLP-16 on a car in the port test; RS-LiDAR-16 on a bus in the indoor parking lot and urban tests) | 6-axis IMU (Xsens MTi-G-710 in the port test; the IMU placed inside the bus is not specified)",{"id":1378,"shortName":1379,"year":509,"sensors":1380,"equipment":1381},"wildcat2022","Wildcat","3D LiDAR Velodyne VLP-16 in two configurations: servo-spun at 0.5 Hz on an inclined mount for 120 deg vertical FoV with the measurement rate set to 20 Hz (SpinningPack), or fixed with the native 30 deg vertical FoV (FlatPack; rate not stated); Ouster OS1-64 at 10 Hz with 120 m range in MulRan DCC03 | IMU (9-DoF 3DM-CV5 at 100 Hz in the SpinningPack; FlatPack IMU model not stated)","lidar | imu | camera | gnss | other | tls_scanner | platform | mobile_scanner_device | compute",{"id":1383,"shortName":1384,"year":876,"sensors":1385,"equipment":1386},"rehder2016spatiotemporal","General spatiotemporal calibration","Aptina MT9V034 WVGA global-shutter cameras at 20 Hz (one in Setup I, two in Setup II) | Analog Devices ADIS16488 (Setup I) and ADIS16448 (Setup II) IMUs at 200 Hz | Hokuyo UTM-30LX 2D laser range finder (Setup II), 270 deg scans at 40 Hz | FPGA-based visual-inertial sensor assigning hardware timestamps","camera | stereo_camera | imu | lidar | other",{"id":1388,"shortName":1389,"year":599,"sensors":1390,"equipment":1391},"voxgraph2020","Voxgraph","3D LiDAR Ouster OS1 (64-beam) in the outdoor MAV field experiment | RGB-D camera Intel RealSense D415 (pointclouds) in the indoor MAV experiment | Odometry input from a time-synchronized camera-IMU running ROVIO visual-inertial odometry; VI-sensor stereo and IMU in the indoor dataset | RTK-GNSS used only as trajectory ground truth","lidar | rgbd | camera | imu | stereo_camera | gnss | platform | compute",{"id":1393,"shortName":1394,"year":509,"sensors":1395,"equipment":1396},"locus2_2022","LOCUS 2.0","one or more 3D LiDARs merged in the body frame (three Velodyne VLP16 on Husky; one lidar on Spot) | IMU for per-lidar motion distortion correction | optional non-lidar odometry (wheel-inertial, kinematic-inertial or visual-inertial) as initial guess through the sensor integration module (LiDAR-centric, loosely coupled)","lidar | imu | wheel_or_leg_odometry | camera | platform | other",{"id":1398,"shortName":1399,"year":599,"sensors":1400,"equipment":1401},"kimera2020","Kimera","monocular camera | stereo | IMU","stereo_camera | imu | other | compute",{"id":1403,"shortName":1404,"year":521,"sensors":675,"equipment":661},"nerfslam2023","NeRF-SLAM",{"id":1406,"shortName":1407,"year":521,"sensors":680,"equipment":1408},"pointslam2023","Point-SLAM","compute | other",{"id":1410,"shortName":1411,"year":509,"sensors":1412,"equipment":642},"schaub2022pc2bim","Point cloud to BIM registration (SLAM tracking)","Ouster OS0-128 Gen 2 LiDAR (128 channels, 90 deg VFOV; 512x20 in env. 1, 1024x20 in env. 2; up to 131,072 points per frame) with IMU data from the sensor",{"id":1414,"shortName":1415,"year":527,"sensors":1416,"equipment":248},"schauer2018peopleremover","Peopleremover","3D laser scans only: Riegl VZ-400 TLS for the authors' lecturehall, campus and wrzburg datasets | third-party datasets of Underwood et al. (sim, lab, carpark; sensor not stated in this paper) | mobile-mapping scan slices from an automotive production line (authors' earlier work) | stated as compatible in principle with RADAR, RGB-D or stereo point clouds (not tested)",{"id":1418,"shortName":1419,"year":492,"sensors":680,"equipment":1420},"badslam2019","BAD SLAM","stereo_camera | camera | rgbd | other | imu | compute",{"id":1422,"shortName":1423,"year":599,"sensors":1424,"equipment":1425},"surfelmeshing2020","SurfelMeshing","RGB-D camera (mainly Microsoft Kinect v1 sequences of the TUM RGB-D benchmark; pre-registered CoRBS sequences; synthetic ICL-NUIM depth with simulated noise)","compute | rgbd",{"id":1427,"shortName":1428,"year":527,"sensors":1429,"equipment":1425},"staticfusion2018","StaticFusion","RGB-D camera, registered RGB-D images at QVGA 320x240 (TUM Freiburg sequences and two hand-held recordings; hand-held camera model not named)",{"id":1431,"shortName":1432,"year":575,"sensors":1433,"equipment":1434},"segal2009gicp","GICP","[\"3D LiDAR (simulated SICK scanner on a rotating joint | roof-mounted Velodyne on an instrumented car)\", \"GPS and IMU (car logs | used only to build ground truth)\"]","lidar | gnss | imu | platform",{"id":1436,"shortName":1437,"year":527,"sensors":1438,"equipment":642},"legoloam2018","LeGO-LOAM","3D LiDAR (Velodyne VLP-16; HDL-64E via KITTI) | IMU (low-cost CH Robotics UM6, used only for initial guess)",{"id":1440,"shortName":1441,"year":599,"sensors":1442,"equipment":939},"liosam2020","LIO-SAM","3D LiDAR (Velodyne VLP-16) | IMU (MicroStrain 3DM-GX5-25) | GNSS (Reach M, optional)",{"id":1444,"shortName":1445,"year":551,"sensors":1446,"equipment":1447},"lvisam2021","LVI-SAM","3D LiDAR (Velodyne VLP-16) | IMU (MicroStrain 3DM-GX5-25) | monocular camera (FLIR BFS-U3-04S2M-CS) | GPS (Reach RS+, ground-truth reference only)","lidar | camera | imu | gnss | platform | compute",{"id":1449,"shortName":1450,"year":492,"sensors":1451,"equipment":1452},"vilslam2019","VIL-SLAM","stereo camera pair (two megapixel cameras; model not reported) | 16 scan-line 3D LiDAR (model not reported) | IMU at 400 Hz (model not reported)","stereo_camera | lidar | imu | compute | other | tls_scanner",{"id":1454,"shortName":1455,"year":498,"sensors":1456,"equipment":1457},"litgs2026","LIT-GS","3D LiDAR (Livox Avia, 10 Hz per Fig. 2) | IMU (built into the Livox Avia, 200 Hz per Fig. 2) | visible-light camera (MV-CA013-21UC) | long-wave thermal imager (MV-CI003-GL-N15, 10 Hz per Fig. 2)","lidar | imu | camera | thermal | other | compute",{"id":1459,"shortName":1460,"year":572,"sensors":131,"equipment":1461},"sibley2010swf","Sliding window filter","stereo_camera | other",{"id":1463,"shortName":1464,"year":796,"sensors":1465,"equipment":1466},"soudarissanane2011scanninggeometry","TLS scanning geometry","terrestrial laser scanner Leica HDS6000 (reference board experiments) | terrestrial laser scanner FARO LS880 HE (room experiment, 1\u002F4 of full resolution)","tls_scanner | other | compute",{"id":1468,"shortName":1469,"year":572,"sensors":1470,"equipment":867},"tinyslam2010","tinySLAM (CoreSLAM)","2D laser scanner (Hokuyo URG-04LX) | wheel odometry (two free odometry wheels with 2000-point encoders) | GPS and compass optionally fused in the particle filter (the compass without good success)",{"id":1472,"shortName":1473,"year":579,"sensors":1474,"equipment":7},"stoyanov2012d2dndt","D2D-NDT","[\"rotating SICK laser (AASS loop and Hannover2 data sets)\", \"simulated 3D range sensor in ROS\u002FGazebo with SICK LMS 200 error models, 180 x 120 deg field of view\"]",{"id":1476,"shortName":1477,"year":515,"sensors":1478,"equipment":1479},"mrsmap2014","MRSMap","RGB-D camera at VGA 640x480 and 30 Hz (TUM Freiburg benchmark sequences and the authors' object dataset; sensor model not named); QVGA used on the robot","compute | platform | other",{"id":1481,"shortName":1482,"year":539,"sensors":1483,"equipment":1484},"stuhrenberg2025liobim","LIO-BIM","3D LiDAR (Velodyne VLP-16) | 9-DoF IMU (LORD MicroStrain 3DM-GX5-25; Xsens MTi-610 in ConSLAM) | camera for AprilTag detection (Intel RealSense D435i; Alvium U-319c 3.2 MP in ConSLAM) | reference: Faro Focus S 70 TLS (office); Leica RTC 360 TLS scans of ConSLAM","platform | lidar | imu | camera | compute | tls_scanner | other",{"id":1486,"shortName":1487,"year":551,"sensors":1488,"equipment":847},"imap2021","iMAP","RGB-D (hand-held Microsoft Azure Kinect for real recordings; rendered Replica RGB-D sequences; TUM RGB-D sequences)",{"id":1490,"shortName":1491,"year":492,"sensors":1492,"equipment":1493},"openvslam2019","OpenVSLAM (stella_vslam)","monocular, stereo or RGB-D camera | camera models: perspective, fisheye, equirectangular (360-degree)","camera | compute | stereo_camera",{"id":1495,"shortName":1496,"year":527,"sensors":1497,"equipment":1498},"smsckf2018","S-MSCKF (msckf_vio)","stereo camera | IMU","stereo_camera | imu | platform | compute | lidar | gnss",{"id":1500,"shortName":1501,"year":533,"sensors":1502,"equipment":1503},"surmann2003_kurt3d","AIS 3D laser robot for indoor digitalization","AIS 3D laser range finder: a 2D laser range finder pitched by a servo, 180 deg (h) x 120 deg (v), with reflectance (2D scanner model not reported) | wheel encoders (odometry) | two 2D safety laser scanners used as bumpers and for dynamic collision avoidance","platform | mobile_scanner_device | lidar | compute",{"id":1505,"shortName":1506,"year":551,"sensors":1507,"equipment":1508},"lion2021","LION","3D LiDAR (model not named) | IMU (model not named)","platform | compute | lidar | imu | wheel_or_leg_odometry",{"id":1510,"shortName":1511,"year":521,"sensors":1512,"equipment":939},"fflins2023","FF-LINS","solid-state non-repetitive LiDAR (Livox Mid-70 in the Robot dataset; Livox Horizon and Livox AVIA in public datasets) | MEMS IMU (ADI ADIS16465 in the Robot dataset; built-in Livox IMUs in public datasets)",{"id":1514,"shortName":1515,"year":498,"sensors":1516,"equipment":1517},"palvio2026","PA-LVIO","3D LiDAR (Livox AVIA 10 Hz on MARS-LVIG, R3LIVE and HandNav data; Hesai AT128 10 Hz on i2Nav-Robot) | IMU (MEMS; BMI088 on MARS-LVIG, R3LIVE and HandNav; ADIS16465 on i2Nav-Robot; 200 Hz) | RGB camera (models not reported; 1280x1024 to 2448x2048 at 10 to 15 Hz)","lidar | camera | imu | platform | gnss | compute",{"id":1519,"shortName":1520,"year":679,"sensors":675,"equipment":1425},"cnnslam2017","CNN-SLAM",{"id":1522,"shortName":1523,"year":551,"sensors":1234,"equipment":1524},"droidslam2021","DROID-SLAM","compute | platform | camera | rgbd",{"id":1526,"shortName":1527,"year":521,"sensors":675,"equipment":661},"dpvo2023","DPVO",{"id":1529,"shortName":1530,"year":1531,"sensors":1532,"equipment":838},"thrun2000_3dmapping","Thrun-Burgard-Fox real-time 2D and 3D laser mapping",2000,"2D laser range finders: forward-looking for 2D mapping and localization, upward-pointed for 3D (model not reported) | wheel odometry (optional; also run with odometry removed)",{"id":1534,"shortName":1535,"year":509,"sensors":1536,"equipment":1537},"kimeramulti2022","Kimera-Multi","stereo images and IMU (Kimera-VIO input) | depth images from an RGB-D camera or from stereo matching, plus 2D semantic segmentation (Kimera-Semantics input) | outdoor experiments: forward-facing RealSense D435i RGBD camera and IMU on a Clearpath Jackal UGV","platform | rgbd | imu | stereo_camera",{"id":1539,"shortName":1540,"year":521,"sensors":1541,"equipment":1542},"vegatorres2023ogm2pgbm","OGM2PGBM","2D LiDAR (simulated Hokuyo UST-10LX) | simulated IMU and wheel odometry","platform | lidar | imu | wheel_or_leg_odometry",{"id":1544,"shortName":1545,"year":655,"sensors":1546,"equipment":1547},"tuna2024xicp","X-ICP","3D LiDAR (Velodyne VLP-16; Ouster OS0-128) | IMU | leg joint encoders (leg odometry prior)","lidar | imu | wheel_or_leg_odometry | platform | tls_scanner | compute",{"id":1549,"shortName":1550,"year":599,"sensors":1497,"equipment":1551},"basalt2020","Basalt","compute | stereo_camera | imu",{"id":1553,"shortName":1554,"year":527,"sensors":1555,"equipment":1556},"pointnetvlad2018","PointNetVLAD","2D LiDAR SICK LMS-151 scans accumulated into 3D reference maps and submaps using GPS\u002FINS (Oxford RobotCar benchmark) | Velodyne-64 LiDAR, as written (in-house U.S., R.A., B.D. sets) | GPS\u002FINS (reference maps in UTM frame and training labels) | stereo camera centre images used only for the NetVLAD image baseline","lidar | gnss | stereo_camera | platform | compute",{"id":1558,"shortName":1559,"year":655,"sensors":1560,"equipment":1561},"vegatorres2024slam2ref","SLAM2REF","3D LiDAR of the ConSLAM handheld system (model not reported; the ISC descriptor requires a 360-degree horizontal FoV) | 9-axis IMU of the ConSLAM handheld system (model not reported; used for DLIO deskewing; LiDAR-IMU extrinsics from OA-LICalib)","lidar | imu | camera | tls_scanner | platform",{"id":1563,"shortName":1564,"year":527,"sensors":1565,"equipment":1408},"supereight2018","supereight","RGB-D camera, depth only (TUM RGB-D real sequences and ICL-NUIM synthetic sequences; sensor models not named)",{"id":1567,"shortName":1568,"year":509,"sensors":1569,"equipment":1570},"vizzo2022vdbfusion","VDBFusion","3D LiDAR | RGB-D","compute | lidar | other | rgbd",{"id":1572,"shortName":1573,"year":679,"sensors":675,"equipment":661},"deepvo2017","DeepVO",{"id":1575,"shortName":1576,"year":492,"sensors":1577,"equipment":1578},"densesurfelmapping2019","Dense Surfel Mapping (Wang, Gao, Shen)","RGB-D (synthetic ICL-NUIM input with ORB-SLAM2 in RGB-D mode) | Stereo camera (KITTI odometry; depth from PSMNet stereo matching, ORB-SLAM2 stereo mode) | Monocular camera with learned depth (KITTI left images with monocular depth prediction; handheld camera with MVDepthNet depth and VINS-Mono tracking)","compute | stereo_camera | camera | platform",{"id":1580,"shortName":1581,"year":599,"sensors":1582,"equipment":1583},"iscloam2020","ISC-LOAM (Intensity Scan Context)","3D LiDAR with intensity (Velodyne VLP-16 on the warehouse AGV; Velodyne HDL-64E in KITTI) | wheel odometry fused with LiDAR odometry for the front-end trajectory in the warehouse test (Sec. IV-B)","lidar | rgbd | wheel_or_leg_odometry | platform | compute | gnss",{"id":1585,"shortName":1586,"year":551,"sensors":1587,"equipment":1588},"floam2021","F-LOAM","3D LiDAR only as method input: Velodyne HDL-64 as written (KITTI, whose cameras and GPS are not used), Velodyne VLP-16 on the warehouse AGV, virtual Velodyne VLP-16 in Gazebo; VICON motion capture used only as indoor ground truth","lidar | camera | gnss | compute | platform | other",{"id":393,"shortName":394,"year":551,"sensors":1590,"equipment":1591},"solid-state LiDAR only (Intel Realsense L515, 70 x 55 deg FoV, 30 Hz)","lidar | other | platform | mobile_scanner_device | compute",{"id":1593,"shortName":1594,"year":551,"sensors":1595,"equipment":535},"pwclonet2021","PWCLO-Net","3D LiDAR point coordinates only (KITTI Velodyne; reflectance not used) (Sec. 4.1)",{"id":1597,"shortName":1598,"year":521,"sensors":680,"equipment":691},"coslam2023","Co-SLAM",{"id":1600,"shortName":1601,"year":521,"sensors":1602,"equipment":1249},"dliom2023","D-LIOM","one or more 3D spinning LiDARs (16-line RoboSense in the authors' device; two Ouster OS1-16 in NTU VIRAL; two inclined 16-line Velodyne in Complex Urban) | 6-axis IMU (built-in consumer-grade IMU at 400 Hz; VectorNav VN100 in VIRAL)",{"id":1604,"shortName":1605,"year":655,"sensors":1606,"equipment":661},"dust3r2024","DUSt3R","monocular camera (unposed, uncalibrated images)",{"id":1608,"shortName":1609,"year":539,"sensors":1129,"equipment":710},"wang2025planarmesh","PlanarMesh",{"id":1611,"shortName":1612,"year":539,"sensors":1613,"equipment":661},"vggt2025","VGGT","monocular camera (one to hundreds of views)",{"id":1615,"shortName":1616,"year":498,"sensors":1617,"equipment":7},"lemon2026","LEMON-Mapping","3D LiDAR: Velodyne (S3E), Avia and Ouster (GEODE), Avia (MARS-LVIG, R3LIVE), Mid360 (self-collected) (Tables I-II)",{"id":1619,"shortName":1620,"year":539,"sensors":1621,"equipment":1622},"lamm2025","LAMM","3D LiDAR of different scanning patterns: Velodyne (KITTI, WildPlaces), Ouster OS2-128 and Livox Avia (HeLiPR), Hesai 128-line (Shenzhen) | four Hikvision cameras on the Shenzhen backpack, used with R3LIVE to produce colored point clouds","compute | platform | lidar | camera",{"id":1624,"shortName":1625,"year":545,"sensors":680,"equipment":959},"elasticfusion2015","ElasticFusion",{"id":1627,"shortName":1628,"year":545,"sensors":680,"equipment":1629},"kintinuous2015","Kintinuous","compute | rgbd | platform | other",{"id":1631,"shortName":1632,"year":521,"sensors":1633,"equipment":1634},"vilens2023","VILENS","IMU (Xsens MTi-100 on ANYmal B300; Epson G365 on ANYmal C100; 400 Hz) | leg kinematics (ANYdrive joint encoders and torque sensors, 400 Hz) | 3D LiDAR (Velodyne VLP-16, 10 Hz) | stereo camera (RealSense D435i gray stereo 848x480 at 30 Hz, or Sevensense Alphasense gray stereo 720x540 at 30 Hz) | monocular fisheye camera (FLIR BFS-U3-16S2C-CS RGB, 1440x1080 at 30 Hz, 150 deg diagonal FoV; SUB configuration)","platform | wheel_or_leg_odometry | lidar | imu | stereo_camera | camera | total_station | tls_scanner | other | compute",{"id":1636,"shortName":1637,"year":655,"sensors":1638,"equipment":1639},"lioekf2024","LIO-EKF","3D LiDAR (dataset sensors; models not named in the paper) | consumer-grade MEMS IMU","platform | imu | compute | lidar",{"id":1641,"shortName":1642,"year":655,"sensors":1643,"equipment":1249},"voxelmappp2024","VoxelMap++","3D LiDAR, spinning or non-repetitive solid-state (Velodyne VLP-32C in M2DGR; Livox HAP in the authors' data) | IMU (Realsense D435i IMU in M2DGR; ZED 2i built-in IMU in the authors' data)",{"id":1645,"shortName":1646,"year":539,"sensors":1647,"equipment":1648},"livgs2025","LiV-GS","3D LiDAR (Livox Horizon in NTU4DRadLM; Livox Avia in R3LIVE hku_park_00) | monocular camera (640 x 480 in NTU4DRadLM; 1280 x 1024 at 30 Hz in hku_park_00)","lidar | camera | radar | platform | compute",{"id":1650,"shortName":1651,"year":539,"sensors":499,"equipment":1652},"gslivm2025","GS-LIVM","lidar | imu | camera | platform | compute",{"id":1654,"shortName":1655,"year":551,"sensors":1656,"equipment":642},"fastlio2021","FAST-LIO","3D LiDAR (solid-state Livox Avia; Velodyne VLP-16 in LINS data) | IMU (model on the authors' rig not reported; Xsens MTiG-710 in LINS data)",{"id":1658,"shortName":1659,"year":492,"sensors":1660,"equipment":681},"xu2019ogmvslam","OGM-enhanced visual SLAM (Xu et al. 2019)","RGB-D camera (Microsoft Kinect, via ROS openni_launch; depth registered to RGB) | virtual 2D laser scan cut from the Kinect point cloud (pointcloud_to_laserscan)",{"id":396,"shortName":397,"year":509,"sensors":1662,"equipment":1249},"3D LiDAR (solid-state Livox Horizon\u002FAvia and spinning Velodyne VLP-16\u002FHDL-32E in the tested datasets) | IMU",{"id":1664,"shortName":1665,"year":679,"sensors":1666,"equipment":661},"psmslam2017","PSM SLAM (Probabilistic Surfel Map)","RGB-D camera (TUM RGB-D real sequences; ICL-NUIM synthetic sequences with noise; sensor models not named)",{"id":1668,"shortName":1669,"year":655,"sensors":842,"equipment":661},"gsslam2024","GS-SLAM (Yan et al.)",{"id":1671,"shortName":1672,"year":498,"sensors":1673,"equipment":1674},"yan2026tunnel","Deep feature-enhanced LVIO (tunnel)","3D LiDAR (mechanical spinning model assumed in the LO derivation; Velodyne on KMCT, Livox on WHU-Helmet) | camera (Intel RealSense D455 RGB-D on KMCT; helmet cameras on WHU-Helmet) | IMU (preintegrated in the VIO)","compute | platform | rgbd | lidar | imu | gnss | camera",{"id":1676,"shortName":1677,"year":498,"sensors":1678,"equipment":1679},"yan2026_underground3dgsslam","Underground RGB-D 3DGS SLAM","RGB-D camera (Kinect2, 512x424 pixels, 10 Hz, 70 x 60 deg field of view)","rgbd | platform | compute | lidar | imu | camera",{"id":1681,"shortName":1682,"year":599,"sensors":1683,"equipment":661},"d3vo2020","D3VO","monocular camera at run time (stereo videos only for self-supervised network training)",{"id":1685,"shortName":1686,"year":509,"sensors":1687,"equipment":847},"voxfusion2022","Vox-Fusion","RGB-D camera (synthetic Replica, ScanNet, iPhone 13 Pro and iPad Pro (2020) with onboard LiDAR depth)",{"id":1689,"shortName":1690,"year":492,"sensors":1691,"equipment":1692},"liomapping2019","LIO-mapping (LIOM)","3D LiDAR (Velodyne VLP-16) | IMU (Xsens MTi-100, 400 Hz)","lidar | imu | other | platform | compute",{"id":1694,"shortName":1695,"year":521,"sensors":1696,"equipment":535},"yin2023semanticbimloc","Semantic localization on BIM maps","3D LiDAR (Velodyne VLP-16)",{"id":1698,"shortName":1699,"year":551,"sensors":1700,"equipment":535},"litamin2_2021","LiTAMIN2","3D spinning LiDAR only (Velodyne HDL-64E S2 in KITTI)",{"id":1702,"shortName":1703,"year":551,"sensors":1704,"equipment":633},"yuan2021lidarcameracalib","livox_camera_calib","Livox Avia solid-state LiDAR (non-repetitive scanning, 20 s accumulation) with Intel RealSense D435i camera (main suite) | Ouster OS2-64 spinning LiDAR with MV-CA013-21UC industrial camera (Sec. IV-C; detailed results in supplementary material)",{"id":1706,"shortName":1707,"year":509,"sensors":1708,"equipment":642},"yuan2022voxelmap","VoxelMap","Velodyne HDL-64E S2 mechanical LiDAR (KITTI, 10 Hz, 360°×32°) | Intel RealSense L515 solid-state LiDAR (30 Hz, 70°×55°) | Livox Avia non-repetitive solid-state LiDAR (10 Hz, 70°×77°) | Livox Avia built-in IMU at 200 Hz, used only in the LiDAR-inertial experiment",{"id":1710,"shortName":1711,"year":521,"sensors":1712,"equipment":1713},"sdvloam2023","SDV-LOAM","monocular grayscale camera | 3D LiDAR (Velodyne HDL-64E on KITTI; VLP-16 on the authors' rig)","camera | lidar | gnss | compute | stereo_camera",{"id":1715,"shortName":1716,"year":655,"sensors":1717,"equipment":857},"srlivo2024","SR-LIVO","3D LiDAR (16-channel OS1 gen1 on NTU-VIRAL; LiDAR written 'LiVOX AVAI' on the R3Live data) | IMU (internal IMU of each LiDAR; LiDAR-IMU extrinsics treated as exact, camera-IMU extrinsics optimized online) | camera (left grayscale camera on NTU-VIRAL; camera of the R3Live handheld rig)",{"id":1719,"shortName":1720,"year":498,"sensors":1721,"equipment":847},"yuan2026_adaptive3dgsslam","Adaptive 3DGS-SLAM (indoor digital twinning)","RGB-D camera: Intel RealSense D435, 1280 x 720 px at 30 frames per second (real-world case) | simulated RGB-D frames from ReplicaCAD (computer experiment)",{"id":1723,"shortName":1724,"year":527,"sensors":1725,"equipment":1726},"yun2018reflection","Glass reflection removal","RIEGL VZ-400 terrestrial laser scanner with multiple echo returns, angular resolution 0.06° x 0.06°","tls_scanner | compute",{"id":1728,"shortName":1729,"year":551,"sensors":1730,"equipment":661},"manhattanslam2021","ManhattanSLAM","RGB-D camera (synthetic ICL-NUIM, TUM RGB-D and TAMU RGB-D sequences; sensor models not named in the paper)",{"id":378,"shortName":379,"year":515,"sensors":1732,"equipment":1733},"3D LiDAR (custom rotating Hokuyo UTM-30LX 2D scanner) | IMU (optional, Xsens MTi-10) | 3D LiDAR (360 deg Velodyne lidar via KITTI, 10 Hz; the model is not named in this paper)","lidar | imu | gnss | other | platform | compute",{"id":1735,"shortName":1736,"year":545,"sensors":1737,"equipment":1738},"vloam2015","V-LOAM","monocular camera (uEye monochrome at 60 Hz; wide-angle lens 76 deg or fisheye lens 185 deg horizontal FoV) | 3D lidar built from a Hokuyo UTM-30LX 2D laser scanner rotated back-and-forth by a motor with encoder (1 s sweep) | KITTI configuration: single camera and Velodyne lidar | no IMU in the method or hardware","camera | lidar | other | platform | compute",{"id":1740,"shortName":1741,"year":679,"sensors":1742,"equipment":1743},"loam2017_auro","LOAM (journal version)","2-axis lidar: back-and-forth spinning Hokuyo UTM-30LX (Sec. 4.1) | continuously spinning Hokuyo on an octo-rotor (Sec. 7.3) | Velodyne HDL-32E (Sec. 7.4) | Velodyne HDL-64E via KITTI (Sec. 7.5) | IMU optional: Xsens MTi-10 (Sec. 7.2), Microstrain 3DM-GX3-45 (Sec. 7.3)","lidar | imu | stereo_camera | gnss | other | platform | compute",{"id":1745,"shortName":1746,"year":527,"sensors":1747,"equipment":1748},"zhang2018lvio","Zhang & Singh LVIO (JFR 2018)","3D LiDAR (Velodyne HDL-32E or Velodyne VLP-16 at 5 Hz; on the handheld Contour a Hokuyo UTM-30LX-EW spun at 1 Hz) | IMU (Xsens MTi-30 at 200 Hz; Xsens MTi-20 on Contour) | monochrome camera (uEye UI-1220SE, 752x480, 76 deg horizontal FoV, 50 Hz) on the two Velodyne suites; on Contour a 640x512 wide-angle camera for motion estimation and a 1600x1200 HD color camera for point colorization","lidar | camera | imu | compute | platform | mobile_scanner_device",{"id":1750,"shortName":1751,"year":515,"sensors":1752,"equipment":1753},"demo2014","DEMO","monocular camera | depth from an RGB-D camera (Xtion Pro Live) or from a 3D LiDAR (rotating Hokuyo UTM-30LX; Velodyne on KITTI)","rgbd | camera | lidar | gnss | compute",{"id":1755,"shortName":1756,"year":876,"sensors":1757,"equipment":1758},"zhang2016degeneracy","Degeneracy factor \u002F solution remapping","monocular camera (uEye monochrome, 60 Hz, 752 x 480, 76 deg horizontal FOV) | custom 3D lidar (Hokuyo UTM-30LX rotated by a motor, 0.25 deg encoder)","camera | lidar | other | platform",{"id":1760,"shortName":1761,"year":521,"sensors":1762,"equipment":661},"goslam2023","GO-SLAM","monocular camera | stereo camera | RGB-D camera",{"id":1764,"shortName":1765,"year":655,"sensors":1766,"equipment":1767},"zhang2024globalbimreg","Global BIM-point registration and association","real site: handheld sensor suite with Ouster OS0-128 LiDAR (clouds built with FAST-LIO2) | simulation: ISPRS indoor modelling benchmark clouds from stationary, handheld and backpack scanners","lidar | compute | tls_scanner | mobile_scanner_device | other",{"id":1769,"shortName":1770,"year":539,"sensors":1771,"equipment":661},"hislam2_2025","HI-SLAM2","monocular RGB camera",{"id":1773,"shortName":1774,"year":498,"sensors":1775,"equipment":1776},"bimloc2026","BIM-Loc (S05)","3D LiDAR (Velodyne VLP-16 in simulation; Livox Mid-360; Ouster OS0-128) | IMU | camera (visualisation only)","lidar | imu | camera | compute",{"id":1778,"shortName":1779,"year":551,"sensors":1780,"equipment":1781},"superodom2021","Super Odometry","3D LiDAR (Velodyne VLP-16) | IMU (Xsens) | fisheye monocular camera","lidar | imu | camera | compute | total_station | platform",{"id":1783,"shortName":1784,"year":655,"sensors":1785,"equipment":1786},"zhao2024deskew","Registration-based deskewing","3D LiDAR Velodyne HDL-32E (UoM indoor data; up to 72,000 points per cloud, 1187 clouds) | No IMU used by the method | Evaluation reference: TLS point cloud (scanner model not reported); the dataset also includes a BIM of the same area, but no evaluation against the BIM is reported","lidar | platform | tls_scanner",{"id":1788,"shortName":1789,"year":492,"sensors":1790,"equipment":1791},"zhen2019tunnellocalizability","Tunnel localizability with LiDAR and UWB","rotating 2D LiDAR (Hokuyo UTM-30LX-EW on a motor rotating 180 deg\u002Fs) | IMU (Microstrain, 100 Hz) | UWB ranging (Pozyx target board on the robot, one anchor in the tunnel)","platform | lidar | imu | uwb | compute",{"id":1793,"shortName":1794,"year":655,"sensors":1795,"equipment":1796},"trajlo2024","Traj-LO","3D LiDAR only (single or multiple, spinning and non-repetitive)","lidar | imu | total_station | other | compute",{"id":1798,"shortName":1799,"year":509,"sensors":1800,"equipment":1801},"fastlivo2022","FAST-LIVO","3D LiDAR (Ouster OS1-16 in NTU-VIRAL; Livox Avia in private data) | IMU | camera","lidar | imu | camera | platform | compute | other | gnss",{"id":1803,"shortName":1804,"year":539,"sensors":1805,"equipment":1806},"fastlivo2_2025","FAST-LIVO2","3D LiDAR (Livox Avia, Ouster OS1-16, Hesai PandarXT-32, Robosense BPearl across datasets) | IMU | camera (pinhole or fisheye)","lidar | camera | platform | imu | other | total_station | compute",{"id":1808,"shortName":1809,"year":521,"sensors":1129,"equipment":1810},"shinemapping2023","SHINE-Mapping","lidar | tls_scanner",{"id":1812,"shortName":1813,"year":527,"sensors":680,"equipment":1814},"zhou2018open3d","Open3D","",{"id":1816,"shortName":1817,"year":551,"sensors":1818,"equipment":1819},"zhou2021planeadjust","Plane-adjustment LiDAR SLAM (indoor)","3D LiDAR only (Velodyne VLP-16 data recorded by a NavVis M6 device)","lidar | mobile_scanner_device | imu | compute",{"id":1821,"shortName":1822,"year":539,"sensors":1823,"equipment":1824},"fastlivo2rc2025","FAST-LIVO2 on Resource-Constrained Platforms","3D LiDAR (Hilti dataset LiDARs | Livox Mid-360 on the authors' rig | a small-FoV AVIA LiDAR, written 'Aivia' in Sec. V-D2, in the degeneration-detection test of Fig. 9, sequence not named) | IMU | camera (B\u002FW fisheye on the authors' rig) | 15 W onboard illuminator for extremely dark scenes","lidar | camera | other | gnss | compute",{"id":1826,"shortName":1827,"year":551,"sensors":1828,"equipment":1829},"camvox2021","CamVox","monocular rolling-shutter camera (MV-CE060-10UC) | solid-state non-repeating-scan LiDAR (Livox Horizon) | IMU (Inertial Sense uINS) used only for LiDAR motion distortion correction","camera | lidar | imu | gnss | rgbd | platform | compute",{"id":1831,"shortName":1832,"year":509,"sensors":680,"equipment":1425},"niceslam2022","NICE-SLAM",{"id":1834,"shortName":1835,"year":509,"sensors":1836,"equipment":1837},"zhu2022liinit","LI-Init","3D LiDAR: Livox Avia (small FoV), Livox Mid360 (non-repetitive scanning), Hesai PandarXT (mechanical spinning); all set to 10 Hz | 6-axis IMU Bosch BMI088, both inside the Livox LiDARs (factory hardware-synchronized) and inside a Pixhawk flight controller (unsynchronized with PandarXT); raw data 200 Hz","lidar | imu | other | compute",{"id":1839,"shortName":1840,"year":655,"sensors":1771,"equipment":1425},"nicerslam2024","NICER-SLAM",{"id":1842,"shortName":1843,"year":521,"sensors":1844,"equipment":1845},"iriom4d2023","4D iRIOM","4D imaging radar (Continental ARS548) | IMU (EPSON G345 inside the Bynav X1-5H GNSS\u002FINS)","radar | imu | gnss | lidar | platform | compute",{"id":1847,"shortName":1848,"year":515,"sensors":1849,"equipment":878},"zlot_bosse2014_mine","CSIRO underground mine CT-SLAM (Northparkes)","rotating SICK LMS 291 2D laser, one revolution every 2 s, spin axis pitched 65 deg from horizontal (Sec. 3.1) | MicroStrain 3DM-GX2 industrial-grade MEMS IMU on the non-spinning part of the mount (Sec. 3.1) | two fixed vertical SICK LMS 291 lasers in a pushbroom configuration, used only for surface reconstruction, not for trajectory estimation (Sec. 3.1, 4.5)",{"id":1851,"shortName":1852,"year":655,"sensors":1147,"equipment":1091},"ltaom2024","LTA-OM",{"id":1854,"shortName":1855,"year":492,"sensors":1856,"equipment":1857},"licfusion2019","LIC-Fusion","3D LiDAR (Velodyne VLP-16) | IMU (Xsens MTi-300) | monochrome global-shutter camera | GNSS RTK (reference only)","imu | lidar | camera | gnss | platform",{"id":1859,"shortName":1860,"year":599,"sensors":1861,"equipment":892},"licfusion2_2020","LIC-Fusion 2.0","3D LiDAR (Velodyne VLP-16, 10 Hz) | IMU (Xsens, model not reported, 400 Hz) | monocular global-shutter camera (model not reported, 20 Hz, 1920x1200)",{"id":1863,"shortName":1864,"year":509,"sensors":546,"equipment":1865},"dmvio2022","DM-VIO","compute | mobile_scanner_device | camera",1790510650039]