[{"data":1,"prerenderedAt":546},["ShallowReactive",2],{"method-superodom2021":3},{"method":4,"reference":56,"equipment":78,"figures":116,"results":153},{"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":26,"sensors":31,"platform":35,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":46,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":50,"relatedVersions":51},"superodom2021","Zhao et al., 2021","Super Odometry","Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments",2021,"recent","C07","odometry_with_local_mapping","Super Odometry 以 IMU 為中心：IMU 里程計提供運動預測給視覺慣性與光達慣性子系統，後兩者回傳相對位姿約束來限制 IMU 偏差，形成由粗到細的估計流程，兼具鬆耦合的容錯與緊耦合的精度。光達端以 PCA 將點分類為點、線、面特徵並做多度量 ICP，地圖以動態八元樹（dynamic octree）組織以降低重建樹的成本。系統部署於 DARPA 地下挑戰賽的無人機與地面機器人。","An IMU-centric factor-graph design in which IMU odometry predicts motion for VIO and LIO and is constrained by their pose outputs, deployed in DARPA SubT underground environments.","full_text_reviewed","peer_reviewed_published","main_body","作者資料含公寓大樓長走廊、樓梯井（受限環境）、暗室與白牆等手持序列，這些序列以全測站（Total Station）追蹤稜鏡提供參考軌跡；粉塵洞穴與 DARPA SubT Urban（廢棄核設施）序列則無軌跡真值，只做定性地圖比較與效能分析。屬既有建物與地下情境，非營建工地。",[20,21,22],"underground_or_tunnel","completed_building","independent_reference",[24,25],"Lowest RMSE ATE among compared methods on every total-station-referenced sequence; the 0.055 m value is for the Long-Corridor sequence (apartment building), with 0.259, 0.156 and 0.174 m on Constrained-Environment, White-Wall and Dark-Room (Sec. V-B, Table II)","Remained operational in dust, dark-room, long-corridor and stair-shaft sequences where some baselines degraded: both vision-only methods failed in the Dust sequence and LOAM showed map misalignment; LiDAR baselines did not fail outright on the total-station sequences (Sec. V-B, Figs. 6-7, Table II)",[27,28,29,30],"Loop closing not used in experiments; odometry-only evaluation (Sec. V-B)","Dust sequence and Urban Circuit run lack trajectory ground truth (Sec. V-A)","Map quality is compared only qualitatively (top-down and z-drift views, Figs. 6 to 7); no quantitative map metric is reported","(inference) Quantitative accuracy rests on four short hand-carried sequences collected by the authors; no public benchmark is used (Sec. 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II-A, IV-A to IV-C)","PCA-based point, line and plane feature classification (linearity, planarity, curvature) with multi-metric point-to-point, point-to-line and point-to-plane ICP against the map through the dynamic octree; each correspondence weighted by how well its neighbours fit the assumed distribution, so noisy returns in dust are down-weighted or rejected (Sec. IV-B, Eqs. 5 to 8); monocular visual features tracked, with LiDAR points in the camera view providing feature depth (Sec. IV-C)","discrete poses; submodules run asynchronously in parallel, each caching constraints and processing them in small batches at its own rate; IMU odometry can output state estimates at 1000 Hz (Sec. V-C2)","not_reported (full text read; no LiDAR motion-compensation step is described)","not used in reported experiments (Sec. V-B)","none in reported experiments","dynamic octree: hash table of voxels each holding its own octree","none","point cloud maps shown qualitatively (Fig. 6, Fig. 7); export not_reported","onboard Intel NUC on the DS drone (Sec. 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Odometry",{"name":7,"methodId":5,"linkable":184,"proposed":74,"self":184},[468,469,470,471,472,473,475,477,479,480,482,484,486,488,489,490,492,493,495,497,499,501,502,504,505,506,507,509,511,512,514,516,518,520,522,524,526],[196,196,196,49,196,196,198,198,196],[200,196,196,49,200,196,198,198,196],[202,196,196,49,202,196,198,198,196],[155,196,196,49,202,196,198,198,196],[206,196,196,49,155,196,198,198,196],[474,196,196,49,202,196,198,198,196],5,[476,196,196,49,206,196,198,198,196],6,[478,196,196,49,474,196,198,198,196],7,[241,196,196,49,200,196,198,198,196],[481,196,196,49,155,196,198,198,196],9,[483,196,196,49,155,196,198,198,196],10,[485,196,196,49,476,196,198,198,196],11,[487,196,196,49,476,196,198,198,196],12,[217,196,196,49,476,196,198,198,196],[209,196,196,49,478,196,198,198,196],[491,196,196,49,200,196,198,198,196],15,[161,196,196,49,241,196,198,198,196],[494,196,196,49,202,196,198,198,196],17,[496,196,196,49,202,196,198,198,196],18,[498,196,196,49,200,196,198,198,196],19,[500,196,196,49,481,196,198,198,196],20,[197,196,196,49,483,196,198,198,196],[503,196,196,49,241,196,198,198,196],22,[222,196,196,49,474,196,198,198,196],[228,196,196,49,241,196,198,198,196],[154,196,196,49,476,196,198,198,196],[508,196,196,49,476,196,198,198,196],26,[510,196,196,49,200,196,198,198,196],27,[215,196,196,49,202,196,198,198,196],[513,196,196,49,200,196,198,198,196],29,[515,196,196,49,200,196,198,198,196],30,[517,196,196,49,200,196,198,198,196],31,[519,196,196,49,476,196,198,198,196],32,[521,196,196,49,241,196,198,198,196],33,[523,196,196,49,241,196,198,198,196],34,[525,196,196,49,241,196,198,198,196],35,[527,196,196,49,478,196,198,198,196],36,[529,530,531,532,533,534,535,536,537,538,539],"success (row covers LOAM and A-LOAM; A-LOAM is the variant discussed in the results)","not_run (incompatible with VLP-16)","success","failed (trajectory estimation)","not_run (incompatible with the testing dataset)","not_run (not integrated with ROS)","not_run (authors could not run the code)","not_run (no publicly available repository)","not_run (inconsistent repository)","not_run (repository no longer available)","success (row covers Fast-LIO 2 and its Scan Context variant)",[367],[],[],[544],"Run outcome ('Result' column) of each reviewed LiDAR-based and combined method on the Luleå tunnel test dataset; '*' marks incompatible with VLP-16, '+' marks not integrated with ROS; some rows combine two methods",[],1790510657159]