[{"data":1,"prerenderedAt":95},["ShallowReactive",2],{"method-tinyslam2010":3},{"method":4,"reference":56,"equipment":77,"figures":94,"results":66},{"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":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},"tinyslam2010","Steux & Hamzaoui, 2010","tinySLAM (CoreSLAM)","tinySLAM: A SLAM algorithm in less than 200 lines C-language program",2010,"classic","C01","odometry_with_local_mapping","tinySLAM 以少於 200 行 C 程式實作雷射 SLAM，核心只有兩個函式：計算掃描與地圖的距離，以及更新地圖。地圖是 1 cm 解析度的網格，每個障礙點不是畫成單一點，而是以修改的 Bresenham 演算法畫出以障礙為尖端的「洞」形函數，使匹配較容易收斂；距離函數則直接加總轉換後掃描端點所在格值。單機版以簡單的蒙地卡羅搜尋做掃描對地圖匹配；也可把距離函數當作粒子濾波的似然函數，以處理歧義、重新定位與里程打滑。","Minimal C laser SLAM using a 1 cm grid of 'hole' functions around obstacle hits, scan-to-map scoring by summing map values at scan endpoints, and Monte Carlo search or particle filtering for pose estimation.","full_text_reviewed","peer_reviewed_published","background","未在營建場域驗證；實驗只在 Mines ParisTech 機電實驗室進行。其極簡程式與低成本短距雷射的組合，對教學或低成本 2D 平面建圖有參考價值，但缺乏定量精度證據，不宜作為工程量測依據（推論）。",[20],"completed_building",[22,23,24,25],"Laser-only speed and yaw-rate estimates matched well-calibrated odometry at up to 2.5 m\u002Fs and 250 deg\u002Fs, with about one frame of latency (Figs. 5-6).","Laser-only tinySLAM ignored an odometry slippage event when the robot hit a wall (Fig. 6).","Loop closure in the laboratory map, combining odometry and laser, is described as almost perfect (Fig. 7).","Displacements below the 1 cm map resolution can be measured because many laser points contribute (Sec. IV).",[27,28,29],"The Hokuyo URG-04LX range (limited to 4 m) and 10 Hz rate are restrictive at 3 m\u002Fs; many readings were zero or ambiguous in the cluttered laboratory (Sec. III).","Compass integration did not work well because of magnetic noise (Sec. IV).","(inference) No quantitative accuracy or runtime evaluation; results are plots and one laboratory map.",[31,32,33],"2D laser scanner (Hokuyo URG-04LX)","wheel odometry (two free odometry wheels with 2000-point encoders)","GPS and compass optionally fused in the particle filter (the compass without good success)",[35],"wheeled UGV (MinesRover, six-wheel rocker-bogie)","stand-alone: simple Monte Carlo search of the pose that best matches the scan to the map; or particle filter in which the scan-to-map distance is each particle's likelihood, with a slippage model (10% of particles stay in place with high noise) (Sec. IV)","no explicit correspondences: the scan-to-map distance sums the map values under the transformed scan endpoints (Algorithm 2)","discrete poses","each scan corrected with a constant longitudinal and rotational speed during the sweep (Sec. III)","none explicit","none","2D grid of 2048 x 2048 16-bit cells at 1 cm per cell; each obstacle hit is drawn as a 'hole' function with its tip at the obstacle using a modified Bresenham ray update and an integration-speed (quality) parameter (Sec. IV; Algorithms 1, 3, 4)","none (a full prior map can be loaded for relocalization in the particle-filter version)","2D grid map and robot trajectory","particle filter run on a desktop PC over a wireless link; the robot's QWERK module (ARM9 with FPGA, 200 MHz) handles sensors and actuators; SLAM runtime not reported (Sec. III; Fig. 3)","https:\u002F\u002Fgithub.com\u002FOpenSLAM-org\u002Fopenslam_tinyslam","MIT (stated on the OpenSLAM.org tinySLAM page; the page also states that commercial use or redistribution is to be arranged with the authors)",[49,52],{"relation":50,"title":51,"doi_or_url":46},"code_release","OpenSLAM tinySLAM source (MIT licence per OpenSLAM page)",{"relation":53,"title":54,"doi_or_url":55},"alias","OpenSLAM page coreslam.html carrying the tinySLAM description","https:\u002F\u002Fopenslam-org.github.io\u002Fcoreslam.html",{"id":5,"kind":57,"shortName":7,"title":8,"authors":58,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":66,"url":67,"firstPublicDate":68,"publicationStatus":16,"metadataStatus":69,"fulltextStatus":15,"era":10,"classicReason":70,"codeUrl":46,"cluster":11,"topics":71,"mdpi":72,"verification":73,"label":6,"fulltextRoute":74,"versionRead":75,"addedByCensus":76},"method",[59,60],"Bruno Steux","Oussama El Hamzaoui","2010 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore","conference","IEEE","pp. 1975-1979","10.1109\u002Ficarcv.2010.5707402",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FICARCV.2010.5707402","2010-12","metadata_verified","reproducible baseline: minimal MIT-licensed grid laser SLAM published with its core source code; the OpenSLAM page 'coreslam.html' carries the same tinySLAM description, authors and paper, which supports treating CoreSLAM as this algorithm, and zou2022lidarslam_indoor Sec. III describes CoreSLAM among ROS 2D SLAM packages (corpus record). The paper itself never uses the name CoreSLAM.",[11],false,"confirmed","NTU institutional (curl)","IEEE Xplore version of record PDF (ICARCV 2010, pp. 1975-1979, 5 pp.)",true,[78,84,89],{"category":79,"model":80,"canonical":80,"role":81,"dataset":66,"specs":82,"locator":83},"lidar","Hokuyo URG-04LX","method input","10 Hz horizontal scan over about 240 deg; maximum range limited to 4 m (another sentence states 5 m); USB","Sec. III; Fig. 1 caption ('HOKUYO URG-04')",{"category":85,"model":86,"canonical":86,"role":81,"dataset":66,"specs":87,"locator":88},"platform","MinesRover","six wheels: four driving and steering, two free odometry wheels; rocker-bogie; 14.8 V 4.1 Ah LiPo; four 45 W motors; top speed 3 m\u002Fs","Sec. III; Figs. 1-2",{"category":90,"model":91,"canonical":91,"role":81,"dataset":66,"specs":92,"locator":93},"wheel_or_leg_odometry","2000 points encoders","on the two free-rotating odometry wheels","Fig. 2 caption",[],1790510662019]