[{"data":1,"prerenderedAt":92},["ShallowReactive",2],{"method-hendrikx2021semanticbimloc":3},{"method":4,"reference":53,"equipment":75,"figures":91,"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":26,"sensors":31,"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},"hendrikx2021semanticbimloc","Hendrikx et al., 2021","Semantic BIM for 2D LiDAR localization","Connecting Semantic Building Information Models and Robotics: An application to 2D LiDAR-based localization",2021,"recent","C11b","localization_in_prior_map_or_bim","作者把 IFC 格式 BIM 中的牆與柱轉成機器人可查詢的語意世界模型：先將一層樓的 IFC 匯出為 IFC-JSON 並加上 JSON-LD 語境，再把柱的斷面輪廓與牆的中心線加厚度改寫成二維幾何（牆轉為共用角點的內外兩條折線），連同與感測器的可感知關係存入 PostgreSQL 與 PostGIS。定位時機器人先查詢附近可由 2D LiDAR 感知的 BIM 物件，再依查詢結果設定線、角與矩形偵測器，只接受支持地圖特徵的量測，並把帶有物件編號的關聯加入 GTSAM 的移動視窗因子圖。作者在埃因霍芬理工大學 Atlas 大樓一層以麥克納姆輪平台遙控三條各約 100 m 的路線示範位姿追蹤，其中一條有三位行人干擾。","Converts IFC walls and columns into a JSON-LD property-graph world model in a PostGIS database; the robot queries nearby BIM features, configures its 2D LiDAR line, corner and box detectors from them, and adds explicit object-level associations to a moving-horizon GTSAM factor graph for pose tracking in a large university building.","full_text_reviewed","peer_reviewed_published","supplementary","在已完工的大學建築中以 IFC 模型定位，示範 BIM 語意物件可直接作為機器人定位地圖；作者也指出設計模型與實況不一致會造成位姿跳動，並提出以語意關聯正確與否重新定義精度的問題（Sec. VI）。",[20],"completed_building",[22,23,24,25],"No false-positive associations with the building model in the experiments (Sec. VI)","Tracked the pose on three routes of about 100 m each, including one with three people walking in the LiDAR field of view (Sec. V; Fig. 4)","Explicit, explainable associations with BIM object ids; individual features can be disabled without removing them from the map by changing their perceivable_by relation (Sec. IV)","Automatic extraction of map features from IFC avoids manual semantic map creation (Sec. II-III)",[27,28,29,30],"BIM model contained spatial inaccuracies (e.g., a square space) that caused pose jumps; recovery mechanisms are future work (Sec. VI; Fig. 6c)","Glass and doors make some walls not always perceivable (Fig. 6c caption)","Corner and column features are seen only from certain positions because of the strict L-shape requirement (Sec. VI)","Initial poses must be provided manually (Sec. V)",[32,33],"2D LiDAR (Hokuyo UTM30-LX, mounted upside down near the floor, 180 deg FOV, 720 points)","wheel encoder odometry",[35],"wheeled UGV (custom platform with mecanum wheels), teleoperated","Moving-horizon factor graph in GTSAM over recent robot poses with range-bearing factors to associated columns and corners and angle-distance factors to walls; horizon truncated to keep N unique semantic map features (three in the experiment) (Sec. IV-V)","map-query-first: features near the current pose (within 6 m) queried from a PostGIS database; line, corner and box features extracted by split-and-merge line fitting and L-shape checks are accepted only if they support a queried BIM feature; associations reference BIM object ids explicitly (Sec. IV)","discrete updates triggered every 15 cm of travel","not_reported","none (localization against the BIM-derived map)","none beyond the moving-horizon graph","JSON-LD property graph of IfcWall and IfcColumn entities with 2D Simple Feature geometry (wall inner and outer polylines with shared corner points, column polygons) stored in PostgreSQL with PostGIS (Sec. III)","IFC building model of one floor (exported to IFC-JSON, framed with the JSON-LD API); manually provided initial poses","2D robot trajectory in the BIM coordinate frame with explicit semantic associations; no point cloud produced","not_reported (C++ implementation with GTSAM)",null,"not_applicable",[49],{"relation":50,"title":51,"doi_or_url":52},"repository_copy","Lirias accepted version record (not read)","https:\u002F\u002Flirias.kuleuven.be\u002Fhandle\u002F20.500.12942\u002F694085",{"id":5,"kind":54,"shortName":7,"title":8,"authors":55,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":46,"url":66,"firstPublicDate":67,"publicationStatus":16,"metadataStatus":68,"fulltextStatus":15,"era":10,"classicReason":47,"codeUrl":46,"cluster":11,"topics":69,"mdpi":70,"verification":71,"label":6,"fulltextRoute":72,"versionRead":73,"addedByCensus":74},"method",[56,57,58,59,60],"R. W. M. Hendrikx","P. Pauwels","E. Torta","H. P. J. Bruyninckx","M. J. G. van de Molengraft","2021 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 11654-11660","10.1109\u002Ficra48506.2021.9561129","https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9561129","2021-05-30","metadata_verified",[11],false,"corrected","NTU institutional (Chrome)","version of record, ICRA 2021 pp. 11654-11660 (IEEE Xplore HTML full text)",true,[76,83,88],{"category":77,"model":78,"canonical":79,"role":80,"dataset":46,"specs":81,"locator":82},"lidar","Hokuyo UTM30-LX","Hokuyo UTM-30LX","method input","2D scanner mounted upside down close to the floor; 180 deg field of view with 720 scan points","Sec. V",{"category":84,"model":85,"canonical":85,"role":80,"dataset":46,"specs":86,"locator":87},"platform","custom-made platform with mecanum wheels","teleoperated on three routes of about 100 m each","Sec. V; Fig. 6a",{"category":89,"model":33,"canonical":33,"role":80,"dataset":46,"specs":90,"locator":82},"wheel_or_leg_odometry","odometry factors in the moving-horizon graph",[],1790510655643]