[{"data":1,"prerenderedAt":97},["ShallowReactive",2],{"method-zhen2019tunnellocalizability":3},{"method":4,"reference":49,"equipment":68,"figures":96,"results":46},{"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":21,"limitations":25,"sensors":30,"platform":34,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":41,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"zhen2019tunnellocalizability","Zhen & Scherer, 2019","Tunnel localizability with LiDAR and UWB","Estimating the Localizability in Tunnel-like Environments using LiDAR and UWB",2019,"recent","C11b","localization_in_prior_map_or_bim","作者把 LiDAR 在先驗地圖中定位的問題寫成一組點落在局部平面上的約束，計算量測距離對位置與姿態擾動的敏感度，分別堆疊成代表力的矩陣 F 與代表力矩的矩陣 T，並把特徵分解後各軸上累積的「虛擬力與力矩」大小定義為可定位性；這個觀點類比於操作力學中無摩擦的力封閉。UWB 測距只提供指向錨點方向的一個力，可補足隧道長軸方向的退化。定位部分以誤差狀態卡爾曼濾波融合 IMU、LiDAR 對地圖的位姿量測，以及由高斯粒子濾波把 UWB 距離轉成的位置量測。作者在卡內基美隆大學 35 m 長的 Smith Hall 隧道中，以改裝的 DJI M100 無人機定性比較有無 UWB 的定位結果。","Localizability model that measures how sensitive LiDAR ranges are to pose perturbations (virtual forces and torques, analogous to force closure) and shows that a single UWB range complements LiDAR along a straight tunnel; an ESKF fuses IMU, scan-to-map LiDAR poses and particle-filtered UWB positions, demonstrated qualitatively on a DJI M100 in a 35 m tunnel.","full_text_reviewed","peer_reviewed_published","background","以隧道巡檢機器人為應用背景，驗證在一段 35 m x 2.4 m x 2.5 m、兩側有管線的校園設施隧道中進行；提供的是幾何退化的量化模型與 UWB 補強概念，沒有軌跡或地圖精度數值（Sec. IV）。",[20],"underground_or_tunnel",[22,23,24],"Localizability metric with a physical interpretation (accumulated virtual forces and torques) that identifies degenerate directions in a prior map (Sec. III-A)","Model predicts the tunnel's weak x-direction and roll constraints and shows that UWB compensates the x-direction (Sec. IV-B, Fig. 6)","With one UWB anchor fused, the UAV localized throughout the tunnel flight, whereas LiDAR-only localization drifted shortly after take-off (Sec. IV-C)",[26,27,28,29],"Localization accuracy was assessed only qualitatively through the reconstructed map, as no motion capture was available (Sec. IV-C)","Localizability in x drops near the single UWB anchor, which is a singular point; more anchors would be needed (Sec. IV-B)","How to combine localizability of several sensor modalities remains unclear (Sec. V)","Requires a prior map and known UWB anchor positions, an overhead for exploration (Sec. V)",[31,32,33],"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)",[35],"UAV (customized DJI Matrice 100, manually flown at about 0.7 m\u002Fs)","Error-state Kalman filter: IMU propagation, 6D pose measurements from matching laser scans to the prior map, and UWB ranges converted to 3D position measurements by a Gaussian particle filter and inverse Kalman update (Sec. III-B)","laser scans matched to the prior map (details deferred to earlier work); localizability model uses plane normals fitted to 20 nearest neighbours of sampled points (Sec. III, IV-B)","discrete filter updates at sensor-specific rates","laser scans projected into the robot body frame using motor encoder angles (Sec. IV-A); motion distortion handling not described","none (localization in a prior map)","none","prior point-cloud map of the tunnel built by aligning multiple local scans with ICP (Sec. IV-A, Fig. 8)","prior tunnel map and the surveyed UWB anchor position in that map","robot trajectory in the prior map; reconstructed map assembled from scans with estimated poses (qualitative)","DJI Manifold on-board computer (2.32 GHz); runtime not reported",null,"not_applicable",[],{"id":5,"kind":50,"shortName":7,"title":8,"authors":51,"year":9,"venue":54,"venueType":55,"publisher":56,"volumeIssuePages":57,"doi":58,"arxivId":46,"url":59,"firstPublicDate":60,"publicationStatus":16,"metadataStatus":61,"fulltextStatus":15,"era":10,"classicReason":47,"codeUrl":46,"cluster":11,"topics":62,"mdpi":63,"verification":64,"label":6,"fulltextRoute":65,"versionRead":66,"addedByCensus":67},"method",[52,53],"Weikun Zhen","Sebastian Scherer","2019 International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 4903-4908","10.1109\u002Ficra.2019.8794167","https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8794167","2019-05-20","metadata_verified",[11],false,"corrected","NTU institutional (Chrome)","version of record, ICRA 2019 pp. 4903-4908 (IEEE Xplore HTML full text)",true,[69,75,81,86,91],{"category":70,"model":71,"canonical":71,"role":72,"dataset":46,"specs":73,"locator":74},"platform","customized DJI Matrice 100 quadrotor","method input","manually flown from the map origin to the far end of the tunnel at about 0.7 m\u002Fs; GPS, compass and gimbal camera not used","Sec. IV-A, IV-C",{"category":76,"model":77,"canonical":78,"role":72,"dataset":46,"specs":79,"locator":80},"lidar","Hokuyo UTM-30LX-EW (rotating)","Hokuyo UTM-30LX","40 Hz, 30 m range; mounted on a motor rotating continuously at 180 deg\u002Fs; scans projected with encoder angles","Sec. IV-A",{"category":82,"model":83,"canonical":84,"role":72,"dataset":46,"specs":85,"locator":80},"imu","Microstrain IMU (model not reported)","MicroStrain IMU (model not reported)","100 Hz",{"category":87,"model":88,"canonical":88,"role":72,"dataset":46,"specs":89,"locator":90},"uwb","Pozyx UWB target board","100 Hz, 100 m range with clear line of sight; one anchor board placed on the tunnel floor at a position measured in the prior map","Sec. IV-A, IV-C; Fig. 5",{"category":92,"model":93,"canonical":93,"role":94,"dataset":46,"specs":95,"locator":80},"compute","DJI Manifold computer","compute for runtime","2.32 GHz",[],1790510662437]