[{"data":1,"prerenderedAt":318},["ShallowReactive",2],{"method-hector2011":3},{"method":4,"reference":53,"equipment":75,"figures":106,"results":107},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":23,"limitations":28,"sensors":31,"platform":34,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":43,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"hector2011","Kohlbrecher et al., 2011","Hector SLAM","A flexible and scalable SLAM system with full 3D motion estimation",2011,"classic","C01","odometry_with_local_mapping","Hector SLAM 結合以 LiDAR 為主的 2D 掃描對地圖（scan-to-map）匹配與以 IMU 為主的 3D 姿態估計：先用估計姿態把掃描轉到穩定座標系，再以 Gauss-Newton 在雙線性內插的佔據網格上求位姿，並用多解析度網格降低陷入局部極小的風險。作者表示在所考慮的小尺度情境中精度足以不需明確的迴圈閉合，並以手持系統在救援競技場與新建築中建圖。","LiDAR scan-to-grid Gauss-Newton matching with multi-resolution maps plus an IMU-based 6-DoF EKF, running without odometry or explicit loop closure in small-scale scenes.","full_text_reviewed","peer_reviewed_published","background","未在施工中工地測試。手持系統曾在 RoboCup 2011 救援競技場與 Dagstuhl 城堡的新建築中建圖，並與參考地圖疊合比對（Fig. 6）；致謝指出 Dagstuhl 的參考資料是由他人提供的樓層平面圖，競技場參考地圖來源未說明。文中只有定性疊合，沒有量化誤差，不能作為工程幾何精度證據。",[20,21,22],"controlled_experiment","completed_building","cross_site",[24,25,26,27],"Low computational cost suitable for embedded, handheld and aerial use: logged data replayed at 3x real time on an Intel Atom Z530 using under half of its resources (abstract","Sec. VI-C) | Does not require wheel odometry or extracted features","worked in unstructured coastal vegetation (Sec. VI-B) | Mounted on a USV without modification and operational in under 30 minutes","the Hokuyo UTM-30LX returns no valid ranges on water, which kept water returns out of SLAM (Sec. VI-B)",[29,30],"Intended for small-scale scenarios where large loops do not have to be closed (Sec. I) | Gradient-based matching is prone to local minima, mitigated but not eliminated by multi-resolution maps (Sec. IV-C) | Gauss-Newton works on non-smooth linear approximations of the map gradient, so local quadratic convergence is not guaranteed (Sec. IV-B) | Full 3D position needs an additional height sensor such as a barometer or range sensor (Sec. V-A) | USV maps were built without GPS","pose-graph optimization with GPS constraints is left for future work (Sec. VI-B) | Authors note standard benchmarks lack the 6-DoF motion and high LiDAR rate they target, so they provide their own bag files instead (Sec. VI)",[32,33],"2D laser range finder","IMU",[35,36,37],"wheeled UGV","handheld","USV (surface vessel)","Gauss-Newton scan-to-map matching on bilinearly interpolated multi-resolution occupancy grids (coarse to fine), with match covariance from the approximate Hessian; 6-DoF EKF navigation filter at 100 Hz with gyro and accelerometer bias states, fused with the SLAM pose by covariance intersection; the EKF pose projected to the plane serves as the scan-matcher start estimate (Sec. IV-B, IV-C, V)","scan endpoints matched to bilinearly interpolated occupancy grid gradients (no feature extraction)","discrete poses","not_reported (scans are transformed to a stabilized frame using estimated attitude; intra-scan motion compensation not described)","none (authors report that explicit loop closing was not required in the considered small-scale scenarios)","none","multi-resolution 2D occupancy grids, used optionally, each coarser level at half the resolution and all levels updated simultaneously from the estimated poses, with matching started at the coarsest level (examples: 20, 10 and 5 cm cells in Fig. 3; 0.25 m grid in the USV map, Fig. 5b) (Sec. IV-C)","2D occupancy grid map and 2D pose; 6-DoF attitude from EKF","logged data processed at 3x real time on an Intel Atom Z530 embedded board, using less than half of that system's resources (Sec. VI.C)","https:\u002F\u002Fgithub.com\u002Ftu-darmstadt-ros-pkg\u002Fhector_slam","BSD (license tag in hector_mapping\u002Fpackage.xml)",[50],{"relation":51,"title":52,"doi_or_url":47},"code_release","hector_slam ROS packages (tu-darmstadt-ros-pkg)",{"id":5,"kind":54,"shortName":7,"title":8,"authors":55,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":65,"url":66,"firstPublicDate":67,"publicationStatus":16,"metadataStatus":68,"fulltextStatus":15,"era":10,"classicReason":69,"codeUrl":47,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[56,57,58,59],"Stefan Kohlbrecher","Oskar von Stryk","Johannes Meyer","Uwe Klingauf","2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","conference","IEEE","pp. 155-160","10.1109\u002Fssrr.2011.6106777",null,"http:\u002F\u002Fmlab-upenn.github.io\u002Ff110\u002Freadings\u002Fdownloads\u002FHectorSLAM11.pdf","2011-11","metadata_verified","reproducible baseline: open-source LiDAR scan-to-map matcher with IMU attitude estimation that runs without odometry and without explicit loop closure (BSD-licensed ROS packages); its frequent use as a handheld 2D baseline is a reviewer judgement not evidenced in this record.",[11],false,"corrected","other","SSRR 2011 proceedings paper as printed (pp. 155-160, IEEE copyright line and ISBN on page 1), third-party course copy; not compared with IEEE Xplore",[76,82,88,92,97,102],{"category":77,"model":78,"canonical":78,"role":79,"dataset":65,"specs":80,"locator":81},"lidar","Hokuyo UTM-30LX","method input","returns no valid distance when beams hit water","Sec. VI-B, VI-C",{"category":83,"model":84,"canonical":84,"role":85,"dataset":65,"specs":86,"locator":87},"compute","Intel Atom Z530 based CPU board","compute for runtime","embedded board of the handheld mapping system","Sec. VI-C",{"category":89,"model":90,"canonical":90,"role":79,"dataset":65,"specs":91,"locator":87},"imu","small low-cost MEMS IMU (model not reported)","not_reported",{"category":93,"model":94,"canonical":94,"role":79,"dataset":65,"specs":95,"locator":96},"mobile_scanner_device","self-contained embedded mapping system for handheld mapping","Hokuyo UTM-30LX, Intel Atom Z530 board and MEMS IMU; can be carried by hand or mounted on vehicles","Sec. VI-C, Fig. 4b",{"category":98,"model":99,"canonical":99,"role":79,"dataset":65,"specs":100,"locator":101},"platform","Hector UGV","LIDAR stabilized about roll and pitch to stay aligned with the ground plane","Sec. VI-A, Fig. 4a",{"category":98,"model":103,"canonical":103,"role":79,"dataset":65,"specs":104,"locator":105},"USV platform (model not reported)","carried the Hector UGV mapping system on Claytor Lake, Virginia","Sec. VI-B, Fig. 4c",[],{"totalRows":108,"groupCount":109,"groups":110,"others":317},12,4,[111,239,266,293],{"slug":112,"group":113,"sourceId":114,"sourceLabel":115,"table":116,"selfRows":117,"metrics":118,"seqs":126,"entrants":133,"cells":157,"outcomes":233,"locators":234,"hardware":235,"wordings":236,"notes":237},"rtabmap2019-table-9","rtabmap2019:Table 9","rtabmap2019","Labbé & Michaud, 2019","Table 9",8,[119,123],{"label":120,"unit":121,"statistic":122,"alignment":91},"ATEend","m","RMSE",{"label":124,"unit":121,"statistic":125,"alignment":91},"ATEmax (maximum per-frame ATE during the run)","max",[127,131],{"dataset":128,"sequence":129,"environment":130},"MIT Stata Center (PR2)","2012-01-25-12-14-25","indoor office building; Long-range lidar",{"dataset":128,"sequence":132,"environment":130},"2012-01-25-12-33-29",[134,137,140,143,145,147,149,151,153,155],{"name":135,"methodId":114,"linkable":136,"proposed":136,"self":71},"RTAB-Map (WheelIMU→S2M) [Long-range lidar]",true,{"name":138,"methodId":139,"linkable":136,"proposed":71,"self":71},"Cartographer (WheelIMU) [Long-range lidar]","cartographer2016",{"name":141,"methodId":142,"linkable":136,"proposed":71,"self":71},"GMapping (WheelIMU) [Long-range lidar]","gmapping2007",{"name":144,"methodId":65,"linkable":71,"proposed":71,"self":71},"Karto SLAM (WheelIMU) [Long-range lidar]",{"name":146,"methodId":5,"linkable":136,"proposed":71,"self":136},"Hector SLAM (no odometry) [Long-range lidar]",{"name":148,"methodId":114,"linkable":136,"proposed":136,"self":71},"RTAB-Map (WheelIMU→S2M) [Short-range lidar]",{"name":150,"methodId":139,"linkable":136,"proposed":71,"self":71},"Cartographer (WheelIMU) [Short-range lidar]",{"name":152,"methodId":142,"linkable":136,"proposed":71,"self":71},"GMapping (WheelIMU) [Short-range lidar]",{"name":154,"methodId":65,"linkable":71,"proposed":71,"self":71},"Karto SLAM (WheelIMU) [Short-range lidar]",{"name":156,"methodId":5,"linkable":136,"proposed":71,"self":136},"Hector SLAM (no odometry) [Short-range lidar]",[158,162,164,166,168,170,171,173,175,178,179,180,182,185,187,189,191,193,195,196,197,199,200,201,202,205,207,209,211,214,215,217,219,221,223,225,226,229,230,232],[159,159,159,160,161,159,161,161,159],0,0.05,-1,[159,163,159,160,161,159,161,161,159],1,[159,159,163,165,161,159,161,161,159],0.08,[159,163,163,167,161,159,161,161,159],0.09,[163,159,159,169,161,159,161,161,159],0.11,[163,163,159,169,161,159,161,161,159],[163,159,163,172,161,159,161,161,159],0.1,[163,163,163,174,161,159,161,161,159],0.12,[176,159,159,177,161,159,161,161,159],2,0.19,[176,163,159,177,161,159,161,161,159],[176,159,163,172,161,159,161,161,159],[176,163,163,181,161,159,161,161,159],0.16,[183,159,159,184,161,159,161,161,159],3,0.22,[183,163,159,186,161,159,161,161,159],0.29,[183,159,163,188,161,159,161,161,159],0.15,[183,163,163,190,161,159,161,161,159],0.17,[109,159,159,192,161,159,161,161,159],0.06,[109,163,159,194,161,159,161,161,159],0.07,[109,159,163,167,161,159,161,161,159],[109,163,163,167,161,159,161,161,159],[198,159,159,194,161,159,161,161,159],5,[198,163,159,165,161,159,161,161,159],[198,159,163,167,161,159,161,161,159],[198,163,163,172,161,159,161,161,159],[203,159,159,204,161,159,161,161,159],6,0.45,[203,163,159,206,161,159,161,161,159],0.52,[203,159,163,208,161,159,161,161,159],0.32,[203,163,163,210,161,159,161,161,159],0.47,[212,159,159,213,161,159,161,161,159],7,1.71,[212,163,159,213,161,159,161,161,159],[212,159,163,216,161,159,161,161,159],0.38,[212,163,163,218,161,159,161,161,159],1.84,[117,159,159,220,161,159,161,161,159],0.48,[117,163,159,222,161,159,161,161,159],0.6,[117,159,163,224,161,159,161,161,159],0.21,[117,163,163,224,161,159,161,161,159],[227,159,159,228,161,159,161,161,159],9,4.59,[227,163,159,228,161,159,161,161,159],[227,159,163,231,161,159,161,161,159],5.53,[227,163,163,231,161,159,161,161,159],[],[116],[],[],[238],"MIT Stata Center 2012-01-25 sequences; RTAB-Map WheelIMU→S2M versus other ROS 2D lidar SLAM run with default parameters; Cartographer, GMapping and Karto use WheelIMU odometry, Hector SLAM uses none; GMapping ATE computed on the current best particle path",{"slug":240,"group":241,"sourceId":5,"sourceLabel":6,"table":242,"selfRows":176,"metrics":243,"seqs":250,"entrants":254,"cells":256,"outcomes":259,"locators":261,"hardware":262,"wordings":263,"notes":264},"hector2011-text-sec-vi-c","hector2011:Text Sec. VI-C","Text Sec. VI-C",[244,247],{"label":245,"unit":246,"statistic":91,"alignment":43},"playback speed without loss of map quality","x real time",{"label":248,"unit":249,"statistic":91,"alignment":43},"share of overall computational resources, stated as less than half","%",[251],{"dataset":252,"sequence":91,"environment":253},"RoboCup 2011 Rescue Arena and Dagstuhl new building logs","indoor arena and building",[255],{"name":7,"methodId":5,"linkable":136,"proposed":136,"self":136},[257,258],[159,159,159,183,161,159,159,161,159],[159,163,159,65,159,159,159,161,159],[260],"stated as less than half of overall resources",[87],[84],[],[265],"Handheld embedded mapping system; logged sensor data replayed to the SLAM system on the Atom Z530 CPU",{"slug":267,"group":268,"sourceId":269,"sourceLabel":270,"table":271,"selfRows":163,"metrics":272,"seqs":275,"entrants":280,"cells":283,"outcomes":285,"locators":287,"hardware":289,"wordings":290,"notes":291},"chen2025quadrupedinspection-table-7","chen2025quadrupedinspection:Table 7","chen2025quadrupedinspection","Chen et al., 2025a","Table 7",[273],{"label":274,"unit":121,"statistic":122,"alignment":91},"Position Drift (m)",[276],{"dataset":277,"sequence":278,"environment":279},"authors' HKUST corridor recording (ROS bag)","single inspection trip, 92.77 m, 195.37 s","indoor academic building (laboratory and office area, congested corridor)",[281],{"name":282,"methodId":5,"linkable":136,"proposed":71,"self":136},"Hector SLAM [Laser]",[284],[159,159,159,65,159,159,161,161,159],[286],"not reported ('-' in all Table 7 cells); Sec. 4.2.2 says Hector SLAM could lose track during direction change and is subject to frequent failure (Fig. 15b)",[288],"Table 7; Sec. 4.2.2",[],[],[292],"Localization RMSE computed with EVO against a reference built from loop closure plus scale alignment to the TLS cloud; one 92.77 m quadruped trip (Go1 Edu) in an academic-building corridor; alignment mode not stated",{"slug":294,"group":295,"sourceId":5,"sourceLabel":6,"table":296,"selfRows":163,"metrics":297,"seqs":301,"entrants":304,"cells":307,"outcomes":310,"locators":311,"hardware":313,"wordings":314,"notes":315},"hector2011-text-sec-v","hector2011:Text Sec. V","Text Sec. V",[298],{"label":299,"unit":300,"statistic":91,"alignment":43},"navigation filter real-time rate","Hz",[302],{"dataset":303,"sequence":303,"environment":303},"not_applicable",[305],{"name":306,"methodId":5,"linkable":136,"proposed":136,"self":136},"navigation filter (EKF)",[308],[159,159,159,309,161,159,161,161,159],100,[],[312],"Sec. V",[],[],[316],"Navigation filter update rate; SLAM pose fused asynchronously",[],1790510655630]