[{"data":1,"prerenderedAt":200},["ShallowReactive",2],{"learn-path-refs":3,"learn-path-recent-window":199},[4,14,22,30,37,45,50,57,63,70,77,84,91,97,103,110,117,124,131,137,143,150,157,162,168,173,180,187,194],{"id":5,"label":6,"shortName":7,"title":8,"year":9,"venue":10,"kind":11,"fulltextStatus":12,"era":13},"cadena2016","Cadena et al., 2016","Past, Present, and Future of SLAM","Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age",2016,"IEEE Transactions on Robotics","survey","full_text_reviewed","classic",{"id":15,"label":16,"shortName":17,"title":18,"year":19,"venue":20,"kind":21,"fulltextStatus":12,"era":13},"curless1996volumetric","Curless & Levoy, 1996","Volumetric range-image integration (TSDF origin; VRIP)","A volumetric method for building complex models from range images",1996,"Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '96)","method",{"id":23,"label":24,"shortName":25,"title":25,"year":26,"venue":27,"kind":28,"fulltextStatus":12,"era":29},"dellaert2017fg","Dellaert & Kaess, 2017","Factor Graphs for Robot Perception",2017,"Foundations and Trends in Robotics","tutorial","recent",{"id":31,"label":32,"shortName":33,"title":34,"year":35,"venue":36,"kind":28,"fulltextStatus":12,"era":13},"durrantwhyte_bailey2006_part1","Durrant-Whyte & Bailey, 2006","SLAM tutorial Part I","Simultaneous localization and mapping: part I",2006,"IEEE Robotics & Automation Magazine",{"id":38,"label":39,"shortName":40,"title":41,"year":42,"venue":43,"kind":44,"fulltextStatus":12,"era":29},"feng2025_construction_lidar_eval","Feng et al., 2025","Feng et al. 2025 construction-site LiDAR SLAM evaluation","Evaluation of LiDAR SLAM algorithms for construction robots in large public construction sites",2025,"Low-carbon Materials and Green Construction","benchmark_or_evaluation",{"id":46,"label":47,"shortName":48,"title":49,"year":26,"venue":10,"kind":21,"fulltextStatus":12,"era":13},"forster2017preint","Forster et al., 2017a","On-manifold IMU preintegration","On-Manifold Preintegration for Real-Time Visual–Inertial Odometry",{"id":51,"label":52,"shortName":53,"title":54,"year":55,"venue":56,"kind":28,"fulltextStatus":12,"era":13},"grisetti2010tutorial","Grisetti et al., 2010","Graph-based SLAM tutorial","A Tutorial on Graph-Based SLAM",2010,"IEEE Intelligent Transportation Systems Magazine",{"id":58,"label":59,"shortName":60,"title":61,"year":42,"venue":62,"kind":44,"fulltextStatus":12,"era":29},"hu2025mapeval","Hu et al., 2025","MapEval","MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework","IEEE Robotics and Automation Letters",{"id":64,"label":65,"shortName":66,"title":67,"year":68,"venue":66,"kind":69,"fulltextStatus":12,"era":13},"jcgm106_2012conformity","JCGM, 2012a","JCGM 106:2012","Evaluation of measurement data – The role of measurement uncertainty in conformity assessment",2012,"standard_or_guideline",{"id":71,"label":72,"shortName":73,"title":74,"year":75,"venue":76,"kind":44,"fulltextStatus":12,"era":29},"keitaanniemi2023drift","Keitaanniemi et al., 2023","ZEB-REVO drift and sectional post-processing","Drift analysis and sectional post-processing of indoor simultaneous localization and mapping (SLAM)-based laser scanning data",2023,"Automation in Construction",{"id":78,"label":79,"shortName":80,"title":81,"year":82,"venue":83,"kind":11,"fulltextStatus":12,"era":29},"lee2024lidarodom_survey","Lee et al., 2024b","LiDAR odometry survey","LiDAR odometry survey: recent advancements and remaining challenges",2024,"Intelligent Service Robotics",{"id":85,"label":86,"shortName":87,"title":88,"year":89,"venue":90,"kind":44,"fulltextStatus":12,"era":13},"magnusson2015beyondpoints","Magnusson et al., 2015","Beyond points: NDT and MUMC vs ICP benchmark (Magnusson et al. 2015)","Beyond points: Evaluating recent 3D scan-matching algorithms",2015,"2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA",{"id":92,"label":93,"shortName":94,"title":95,"year":42,"venue":96,"kind":44,"fulltextStatus":12,"era":29},"potokar2025lo_eval","Potokar & Kaess, 2025","LO component evaluation (Potokar and Kaess)","A Comprehensive Evaluation of LiDAR Odometry Techniques","2025 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)",{"id":98,"label":99,"shortName":100,"title":101,"year":26,"venue":76,"kind":102,"fulltextStatus":12,"era":29},"rebolj2017pcqualityscanvsbim","Rebolj et al., 2017","Rebolj et al. 2017 (point cloud quality for Scan-vs-BIM)","Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring","application_study",{"id":104,"label":105,"shortName":106,"title":107,"year":108,"venue":109,"kind":44,"fulltextStatus":12,"era":13},"rusinkiewicz2001variants","Rusinkiewicz & Levoy, 2001","Efficient ICP variants","Efficient variants of the ICP algorithm",2001,"Proceedings Third International Conference on 3-D Digital Imaging and Modeling (3DIM)",{"id":111,"label":112,"shortName":113,"title":114,"year":115,"venue":116,"kind":21,"fulltextStatus":12,"era":13},"segal2009gicp","Segal et al., 2009","GICP","Generalized-ICP",2009,"Robotics: Science and Systems V",{"id":118,"label":119,"shortName":120,"title":121,"year":122,"venue":123,"kind":21,"fulltextStatus":12,"era":29},"liosam2020","Shan et al., 2020","LIO-SAM","LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping",2020,"2020 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)",{"id":125,"label":126,"shortName":127,"title":128,"year":129,"venue":130,"kind":21,"fulltextStatus":12,"era":13},"smith_self_cheeseman1990","Smith et al., 1990","Stochastic map","Estimating Uncertain Spatial Relationships in Robotics",1990,"Autonomous Robot Vehicles (book, Springer New York)",{"id":132,"label":133,"shortName":134,"title":135,"year":68,"venue":136,"kind":44,"fulltextStatus":12,"era":13},"sturm2012tum","Sturm et al., 2012","TUM RGB-D (ATE\u002FRPE)","A benchmark for the evaluation of RGB-D SLAM systems","2012 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)",{"id":138,"label":139,"shortName":140,"title":141,"year":142,"venue":10,"kind":11,"fulltextStatus":12,"era":29},"tosi2026survey","Tosi et al., 2026","NeRF\u002F3DGS-SLAM survey (Tosi et al.)","How NeRFs and 3-D Gaussian Splatting Are Reshaping SLAM: A Survey",2026,{"id":144,"label":145,"shortName":146,"title":147,"year":75,"venue":148,"kind":149,"fulltextStatus":12,"era":29},"trzeciak2023conslam","Trzeciak et al., 2023","ConSLAM","ConSLAM: Construction Data Set for SLAM","Journal of Computing in Civil Engineering","dataset",{"id":151,"label":152,"shortName":153,"title":154,"year":155,"venue":156,"kind":21,"fulltextStatus":12,"era":29},"vizzo2021puma","Vizzo et al., 2021","PUMA","Poisson Surface Reconstruction for LiDAR Odometry and Mapping",2021,"2021 IEEE International Conference on Robotics and Automation (ICRA)",{"id":158,"label":159,"shortName":160,"title":161,"year":75,"venue":62,"kind":21,"fulltextStatus":12,"era":29},"kissicp2023","Vizzo et al., 2023","KISS-ICP","KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way",{"id":163,"label":164,"shortName":165,"title":166,"year":167,"venue":10,"kind":21,"fulltextStatus":12,"era":29},"fastlio2_2022","Xu et al., 2022","FAST-LIO2","FAST-LIO2: Fast Direct LiDAR-Inertial Odometry",2022,{"id":169,"label":170,"shortName":171,"title":172,"year":82,"venue":76,"kind":11,"fulltextStatus":12,"era":29},"yarovoi2024review","Yarovoi & Cho, 2024","Yarovoi & Cho 2024","Review of simultaneous localization and mapping (SLAM) for construction robotics applications",{"id":174,"label":175,"shortName":176,"title":177,"year":178,"venue":179,"kind":28,"fulltextStatus":12,"era":29},"zhang2018trajeval","Zhang & Scaramuzza, 2018","Trajectory-evaluation tutorial","A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry",2018,"2018 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)",{"id":181,"label":182,"shortName":183,"title":184,"year":185,"venue":186,"kind":21,"fulltextStatus":12,"era":13},"loam2014","Zhang & Singh, 2014","LOAM","LOAM: Lidar Odometry and Mapping in Real-time",2014,"Robotics: Science and Systems X (RSS 2014)",{"id":188,"label":189,"shortName":190,"title":191,"year":9,"venue":192,"kind":193,"fulltextStatus":12,"era":13},"zhang2016degeneracy","Zhang et al., 2016","Degeneracy factor \u002F solution remapping","On degeneracy of optimization-based state estimation problems","2016 IEEE International Conference on Robotics and Automation (ICRA)","component",{"id":195,"label":196,"shortName":197,"title":198,"year":75,"venue":62,"kind":149,"fulltextStatus":12,"era":29},"zhang2023hiltioxford","Zhang et al., 2023c","Hilti-Oxford (Hilti 2022)","Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping","2016-09-25",1790510653090]