[{"data":1,"prerenderedAt":118},["ShallowReactive",2],{"method-compslam2020":3},{"method":4,"reference":58,"equipment":83,"figures":117,"results":72},{"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":22,"limitations":27,"sensors":32,"platform":37,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"compslam2020","Khattak et al., 2020","CompSLAM","Complementary Multi-Modal Sensor Fusion for Resilient Robot Pose Estimation in Subterranean Environments",2020,"recent","C07","odometry_with_local_mapping","CompSLAM 的 ICUAS 版本以鬆耦合方式，把視覺慣性里程計（ROVIO）或熱影像慣性里程計（ROTIO，使用完整輻射溫度影像）接到 LOAM 式 LiDAR 里程計與建圖：相機里程計在新點雲到達時提供掃描對掃描配準的初始值，並以 J^T J 的特徵值判斷掃描對掃描與掃描對地圖配準是否退化；一旦退化，就改用相機里程計的相對位移延續 LiDAR 位姿，並把當前點雲寫入地圖。相機里程計本身以共變異數成長的 D-optimality 與運動界限做健康檢查。熱影像在黑暗與粉塵中仍能提供約束，是本文與僅用可見光相機融合的主要差別。","Loosely coupled, degeneracy-aware fusion in which visual-inertial or thermal-inertial odometry provides priors to LOAM-style LiDAR odometry and mapping and takes over pose propagation when the LiDAR problem becomes ill-conditioned, with a D-optimality health check on the camera odometry.","full_text_reviewed","peer_reviewed_published","supplementary","ICUAS 論文的場域為高速公路地下道、營運中的地下礦坑與 DARPA SubT 隧道，均無外部真值，屬定性驗證，沒有施工現場點雲精度證據。2025 年系統預印本指出 CompSLAM 曾用於長期營建作業與工業開挖等後續專案，並以 MapEval 及 DARPA 真值點雲評估 SubT 決賽場地的地圖（arXiv 2505.06483 Tables II-IV）；同組織的營建機器人融合研究可見 [nubert2022constructionfusion]。熱影像與 LiDAR 互補的設計可供粉塵或無照明的地下工程參考（推論）。",[20,21],"underground_or_tunnel","task_level_validation",[23,24,25,26],"In a self-similar highway underpass the LiDAR-only map is wrong in size, while the proposed fusion builds a correct map (Sec. IV-A; Fig. 5)","Autonomous flight of about 410 m in an active underground mine in darkness and heavy airborne dust that returned to its take-off position (Sec. IV-B)","Used as the onboard localization in an autonomous aerial mission of about 190 m in the DARPA SubT Tunnel Circuit, returning to the take-off spot (Sec. IV-C)","The 2025 successor preprint reports deployment on all aerial, legged and wheeled robots of Team Cerberus in their competition-winning DARPA SubT final run (arXiv 2505.06483 abstract)",[28,29,30,31],"Evaluation in the ICUAS paper is qualitative; drift is judged only by the autonomous return to the take-off position in the absence of external ground truth (Sec. IV-B, IV-C)","Thermal imagery becomes scarce in thermally flat scenes with small temperature variations (Sec. I; Sec. II)","Visible-light cameras degrade in poor illumination, low texture and obscurants; LiDAR degrades in self-similar geometry and in dust or fog (Sec. I)","The 2025 system version does not filter dynamic objects (arXiv 2505.06483 Sec. V)",[33,34,35,36],"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",[38],"UAV (quadrotor based on DJI Matrice M100 in the underpass test; aerial robots in the mine and in the SubT Tunnel Circuit)","loosely coupled, degeneracy-aware: ROVIO-based visual-inertial odometry, or its thermal variant on full radiometric thermal images (ROTIO), supplies the relative motion between successive point clouds as the prior for LOAM scan-to-scan matching; degeneracy of scan-to-scan and scan-to-map matching is detected from the eigenvalues of J^T J, and in degenerate steps the previous LiDAR odometry or mapping estimate is propagated with the camera-odometry relative transform; camera odometry is health-checked by the relative growth of its covariance (D-optimality) and by motion bounds (Sec. III, Eqs. 1-4, Fig. 4)","LOAM point-to-line and point-to-plane correspondences for scan-to-scan and scan-to-map matching (Sec. III)","discrete per-scan poses; the higher-rate camera odometry is used to compute the relative transform between successive point clouds (Sec. III)","not_reported in the ICUAS paper","none reported in the ICUAS paper","LOAM point-cloud map; in ill-conditioned scan-to-map steps the current cloud is inserted with the prior mapping estimate plus the camera-odometry relative transform (Sec. III)","none","robot pose and LiDAR point-cloud map (Figs. 5-7)","onboard Intel NUC-i7 (NUC7i7BNH) on the underpass UAV, real time fully onboard (Sec. IV-A); no timing figures reported","https:\u002F\u002Fgithub.com\u002Fleggedrobotics\u002Fcompslam_subt","BSD-3-Clause (GitHub license metadata; code released in 2025 with the successor preprint, not with the ICUAS paper)",[51,55],{"relation":52,"title":53,"doi_or_url":54},"successor","CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments (Khattak, Homberger, Bernreiter, Nubert, Andersson, Siegwart, Alexis, Hutter; arXiv 2505.06483v1, 2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.06483",{"relation":56,"title":57,"doi_or_url":48},"code_release","leggedrobotics\u002Fcompslam_subt (released with the 2025 preprint)",{"id":5,"kind":59,"shortName":7,"title":60,"authors":61,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":73,"firstPublicDate":74,"publicationStatus":16,"metadataStatus":75,"fulltextStatus":15,"era":10,"classicReason":76,"codeUrl":48,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":82},"method","Complementary Multi–Modal Sensor Fusion for Resilient Robot Pose Estimation in Subterranean Environments",[62,63,64,65,66],"Shehryar Khattak","Huan Nguyen","Frank Mascarich","Tung Dang","Kostas Alexis","2020 International Conference on Unmanned Aircraft Systems (ICUAS)","conference","IEEE","pp. 1024-1029","10.1109\u002Ficuas48674.2020.9213865",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FICUAS48674.2020.9213865","2020-09","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","IEEE version of record (ICUAS 2020 full-text HTML via NTU access); successor preprint arXiv 2505.06483v1 read in part for context",true,[84,90,94,100,104,109,113],{"category":85,"model":86,"canonical":86,"role":87,"dataset":72,"specs":88,"locator":89},"platform","quadrotor based on DJI Matrice M100","method input","aerial robot used in the self-similar underpass experiment","Sec. IV-A",{"category":91,"model":92,"canonical":92,"role":87,"dataset":72,"specs":93,"locator":89},"lidar","Velodyne PuckLITE","point clouds at 10 Hz",{"category":95,"model":96,"canonical":97,"role":87,"dataset":72,"specs":98,"locator":99},"imu","VectorNav VN-100","VectorNav VN100","inertial measurements at 200 Hz","Sec. IV-A; Sec. IV-B",{"category":101,"model":102,"canonical":102,"role":87,"dataset":72,"specs":103,"locator":89},"camera","FLIR Blackfly (with shutter-synchronized LEDs)","images at 20 Hz",{"category":105,"model":106,"canonical":106,"role":107,"dataset":72,"specs":108,"locator":89},"compute","Intel NUC-i7 (NUC7i7BNH)","compute for runtime","onboard computer running pose estimation in real time",{"category":91,"model":110,"canonical":110,"role":87,"dataset":72,"specs":111,"locator":112},"Ouster OS1-64","64 beams; used with LOAM in the TRJV mine deployment","Sec. IV-B",{"category":114,"model":115,"canonical":115,"role":87,"dataset":72,"specs":116,"locator":112},"thermal","FLIR Tau2","full radiometric thermal imagery for ROTIO",[],1790510662250]