[{"data":1,"prerenderedAt":501},["ShallowReactive",2],{"method-pings2025":3},{"method":4,"reference":55,"equipment":79,"figures":125,"results":126},{"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":27,"sensors":31,"platform":35,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"pings2025","Pan et al., 2025","PINGS","PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map",2025,"recent","C09","full_slam_with_global_correction","PINGS 在 PIN-SLAM 的神經點上同時編碼連續 SDF 與高斯潑濺輻射場，並加上兩者之間的幾何一致性約束，使影像的稠密光度線索回饋改善距離場，距離場則約束高斯分布。作者在 Oxford Spires 以 Leica RTC360 地面雷射掃描（TLS）參考地圖評估表面重建，但該評估關閉定位模組、全部使用真值位姿，因此量到的是建圖元件品質。","Unifies an SDF and a Gaussian radiance field in one neural point map with mutual geometric consistency for LiDAR-visual SLAM.","full_text_reviewed","peer_reviewed_published","main_body","論文未在營建場域測試；以 TLS 參考的校園資料（Oxford Spires）評估幾何，方法設計（LiDAR 幾何＋影像外觀）與既有建物紀錄高度相關（推論）。",[20,21,22],"public_benchmark","independent_reference","controlled_experiment",[24,25,26],"Better Chamfer distance and F-score than OpenMVS, Nerfacto, GSS, VDB-Fusion and PIN-SLAM on Oxford Spires (Sec. IV-C; Table II)","Radiance-field cues improve SDF accuracy over PIN-SLAM at equal resolution (Sec. IV-C)","Lower drift and ATE than PIN-SLAM on two long in-house car sequences, e.g. ATE 1.99 m vs 3.17 m on the 5.0 km sequence (Table III)",[28,29,30],"~5 s per frame overall on an A6000 (Sec. V)","No pre-trained priors (Sec. V)","No explicit 4D (dynamic) modelling (Sec. V)",[32,33,34],"Ouster OS1-128 LiDAR, 128 beams, 45 deg vertical FOV, 10 Hz, mounted horizontally (in-house car dataset)","four Basler Ace cameras giving 360 deg coverage at 10 Hz (in-house car dataset)","Oxford Spires handheld rig: 64-beam LiDAR and three global-shutter cameras (dataset)",[36,37],"vehicle","handheld","LiDAR odometry by Gauss-Newton alignment of each scan to the SDF zero level set using only SDF values and gradients (no explicit correspondences); camera poses initialised from LiDAR odometry and extrinsics and refined by gradient descent during radiance-field training to absorb imperfect camera and LiDAR synchronization; loop closure detection and pose graph optimization run in parallel","point-to-implicit SDF for odometry; photometric Gaussian-splatting loss with SDF-radiance geometric consistency for mapping","discrete poses","not_reported","loop closure detection running in parallel (inherited from PIN-SLAM)","pose graph optimization","neural points jointly encoding a signed distance field and a Gaussian-splatting radiance field","none (per-scene online optimization, no pre-trained priors)","SDF mesh via marching cubes; rendered RGB and depth from the Gaussian radiance field","SDF mapping and LiDAR odometry at sensor rate, but overall ~5 s per frame on an NVIDIA A6000 due to radiance-field mapping (Sec. V)","https:\u002F\u002Fgithub.com\u002FPRBonn\u002FPINGS","MIT",[51],{"relation":52,"title":53,"doi_or_url":54},"preprint","arXiv:2502.05752","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.05752",{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":65,"venueType":66,"publisher":67,"volumeIssuePages":41,"doi":68,"arxivId":69,"url":70,"firstPublicDate":71,"publicationStatus":16,"metadataStatus":72,"fulltextStatus":15,"era":10,"classicReason":73,"codeUrl":48,"cluster":11,"topics":74,"mdpi":75,"verification":76,"label":6,"fulltextRoute":77,"versionRead":78,"addedByCensus":75},"method",[58,59,60,61,62,63,64],"Yue Pan","Xingguang Zhong","Liren Jin","Louis Wiesmann","Marija Popović","Jens Behley","Cyrill Stachniss","Robotics: Science and Systems XXI","conference","RSS Foundation","10.15607\u002Frss.2025.xxi.040","2502.05752","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.15607\u002Frss.2025.xxi.040","2025-02-09","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v2 (2025-09-09, 15 pages, 'presented at RSS 2025') read in full; the RSS XXI proceedings PDF (roboticsproceedings.org\u002Frss21\u002Fp040.pdf) was downloaded but yielded no extractable text (image-only or damaged PDF), so it was not compared",[80,87,92,97,102,106,110,114,117],{"category":81,"model":82,"canonical":82,"role":83,"dataset":84,"specs":85,"locator":86},"lidar","Ouster OS1-128","method input","in-house car dataset","128 beams, 45 deg vertical FOV, 10 Hz, mounted horizontally on a robot car","Sec. IV-A1",{"category":88,"model":89,"canonical":89,"role":83,"dataset":84,"specs":90,"locator":91},"camera","Basler Ace (four units)","360 deg visual coverage, 10 Hz; images used at 512 x 1,032","Sec. IV-A1; Sec. IV-A2",{"category":93,"model":94,"canonical":94,"role":83,"dataset":84,"specs":95,"locator":96},"platform","robot car","drives of 5.0 km (about 10,000 scans and 40,000 images) and 3.7 km","Sec. IV-A1; Table III",{"category":98,"model":99,"canonical":99,"role":100,"dataset":84,"specs":101,"locator":86},"gnss","RTK-GNSS (model not named)","reference or ground truth","incorporated into offline LiDAR bundle adjustment for reference poses",{"category":103,"model":104,"canonical":104,"role":100,"dataset":84,"specs":105,"locator":86},"tls_scanner","geo-referenced terrestrial laser scans (scanner model not named)","precise constraints in the offline reference-pose bundle adjustment",{"category":103,"model":107,"canonical":107,"role":100,"dataset":108,"specs":109,"locator":86},"Leica RTC360","Oxford Spires","millimetre-accurate reference map",{"category":81,"model":111,"canonical":111,"role":112,"dataset":108,"specs":113,"locator":86},"64-beam LiDAR (model not named)","dataset sensor","on a handheld system",{"category":88,"model":115,"canonical":115,"role":112,"dataset":108,"specs":116,"locator":91},"three global-shutter cameras (model not named)","images used at 540 x 720",{"category":118,"model":119,"canonical":120,"role":121,"dataset":122,"specs":123,"locator":124},"compute","NVIDIA A6000","NVidia A6000","compute for runtime",null,"single GPU; overall about 5 s per frame","Sec. IV-A2; Sec. V",[],{"totalRows":127,"groupCount":128,"groups":129,"others":500},25,3,[130,369,477],{"slug":131,"group":132,"sourceId":5,"sourceLabel":6,"table":133,"selfRows":134,"metrics":135,"seqs":146,"entrants":156,"cells":172,"outcomes":363,"locators":364,"hardware":365,"wordings":366,"notes":367},"pings2025-table-ii","pings2025:Table II","Table II",16,[136,139,141,143],{"label":137,"unit":138,"statistic":41,"alignment":41},"Accuracy error","m",{"label":140,"unit":138,"statistic":41,"alignment":41},"Completeness error",{"label":142,"unit":138,"statistic":41,"alignment":41},"Chamfer Distance",{"label":144,"unit":145,"statistic":41,"alignment":41},"F-score (0.1 m threshold)","fraction (0 to 1)",[147,150,152,154],{"dataset":108,"sequence":148,"environment":149},"Blenheim Palace 05","Oxford Spires sequences Blenheim Palace 05, Christ Church 02, Keble College 04 and Observatory Quarter 01, handheld LiDAR-camera rig; scene type not described in the paper",{"dataset":108,"sequence":151,"environment":149},"Christ Church 02",{"dataset":108,"sequence":153,"environment":149},"Keble College 04",{"dataset":108,"sequence":155,"environment":149},"Observatory Quarter 01",[157,159,161,163,167,170],{"name":158,"methodId":122,"linkable":75,"proposed":75,"self":75},"OpenMVS [5] (offline)",{"name":160,"methodId":122,"linkable":75,"proposed":75,"self":75},"Nerfacto [62] (offline)",{"name":162,"methodId":122,"linkable":75,"proposed":75,"self":75},"GSS [11]",{"name":164,"methodId":165,"linkable":166,"proposed":75,"self":75},"VDB-Fusion [67]","vizzo2022vdbfusion",true,{"name":168,"methodId":169,"linkable":166,"proposed":75,"self":75},"PIN-SLAM [51]","pinslam2024",{"name":171,"methodId":5,"linkable":166,"proposed":166,"self":166},"PINGS (Ours)",[173,177,180,183,185,187,189,191,193,195,197,199,201,203,205,207,209,212,214,216,218,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,252,254,256,258,260,262,264,266,268,270,272,274,275,277,279,281,283,285,287,289,290,292,294,296,298,300,301,303,305,307,308,310,312,314,316,318,320,321,323,325,327,329,331,333,335,337,339,341,342,344,346,348,350,352,354,356,357,359,361],[174,174,174,175,176,174,176,176,174],0,0.126,-1,[174,178,174,179,176,174,176,176,174],1,1.045,[174,181,174,182,176,174,176,176,174],2,0.586,[174,128,174,184,176,174,176,176,174],0.458,[178,174,174,186,176,174,176,176,174],0.302,[178,178,174,188,176,174,176,176,174],0.676,[178,181,174,190,176,174,176,176,174],0.489,[178,128,174,192,176,174,176,176,174],0.309,[181,174,174,194,176,174,176,176,174],0.204,[181,178,174,196,176,174,176,176,174],0.254,[181,181,174,198,176,174,176,176,174],0.229,[181,128,174,200,176,174,176,176,174],0.266,[128,174,174,202,176,174,176,176,174],0.098,[128,178,174,204,176,174,176,176,174],0.123,[128,181,174,206,176,174,176,176,174],0.111,[128,128,174,208,176,174,176,176,174],0.692,[210,174,174,211,176,174,176,176,174],4,0.078,[210,178,174,213,176,174,176,176,174],0.136,[210,181,174,215,176,174,176,176,174],0.107,[210,128,174,217,176,174,176,176,174],0.739,[219,174,174,220,176,174,176,176,174],5,0.072,[219,178,174,222,176,174,176,176,174],0.133,[219,181,174,224,176,174,176,176,174],0.102,[219,128,174,226,176,174,176,176,174],0.758,[174,174,178,228,176,174,176,176,174],0.046,[174,178,178,230,176,174,176,176,174],5.381,[174,181,178,232,176,174,176,176,174],2.714,[174,128,178,234,176,174,176,176,174],0.41,[178,174,178,236,176,174,176,176,174],0.219,[178,178,178,238,176,174,176,176,174],4.435,[178,181,178,240,176,174,176,176,174],2.327,[178,128,178,242,176,174,176,176,174],0.343,[181,174,178,244,176,174,176,176,174],0.174,[181,178,178,246,176,174,176,176,174],0.292,[181,181,178,248,176,174,176,176,174],0.233,[181,128,178,250,176,174,176,176,174],0.346,[128,174,178,202,176,174,176,176,174],[128,178,178,253,176,174,176,176,174],0.243,[128,181,178,255,176,174,176,176,174],0.171,[128,128,178,257,176,174,176,176,174],0.582,[210,174,178,259,176,174,176,176,174],0.069,[210,178,178,261,176,174,176,176,174],0.252,[210,181,178,263,176,174,176,176,174],0.16,[210,128,178,265,176,174,176,176,174],0.617,[219,174,178,267,176,174,176,176,174],0.067,[219,178,178,269,176,174,176,176,174],0.251,[219,181,178,271,176,174,176,176,174],0.159,[219,128,178,273,176,174,176,176,174],0.622,[174,174,181,267,176,174,176,176,174],[174,178,181,276,176,174,176,176,174],0.342,[174,181,181,278,176,174,176,176,174],0.205,[174,128,181,280,176,174,176,176,174],0.806,[178,174,181,282,176,174,176,176,174],0.137,[178,178,181,284,176,174,176,176,174],0.15,[178,181,181,286,176,174,176,176,174],0.144,[178,128,181,288,176,174,176,176,174],0.68,[181,174,181,255,176,174,176,176,174],[181,178,181,291,176,174,176,176,174],0.162,[181,181,181,293,176,174,176,176,174],0.167,[181,128,181,295,176,174,176,176,174],0.466,[128,174,181,297,176,174,176,176,174],0.103,[128,178,181,299,176,174,176,176,174],0.101,[128,181,181,224,176,174,176,176,174],[128,128,181,302,176,174,176,176,174],0.719,[210,174,181,304,176,174,176,176,174],0.096,[210,178,181,306,176,174,176,176,174],0.108,[210,181,181,224,176,174,176,176,174],[210,128,181,309,176,174,176,176,174],0.744,[219,174,181,311,176,174,176,176,174],0.093,[219,178,181,313,176,174,176,176,174],0.106,[219,181,181,315,176,174,176,176,174],0.099,[219,128,181,317,176,174,176,176,174],0.749,[174,174,128,319,176,174,176,176,174],0.048,[174,178,128,273,176,174,176,176,174],[174,181,128,322,176,174,176,176,174],0.335,[174,128,128,324,176,174,176,176,174],0.734,[178,174,128,326,176,174,176,176,174],0.197,[178,178,128,328,176,174,176,176,174],0.398,[178,181,128,330,176,174,176,176,174],0.298,[178,128,128,332,176,174,176,176,174],0.592,[181,174,128,334,176,174,176,176,174],0.179,[181,178,128,336,176,174,176,176,174],0.184,[181,181,128,338,176,174,176,176,174],0.181,[181,128,128,340,176,174,176,176,174],0.407,[128,174,128,204,176,174,176,176,174],[128,178,128,343,176,174,176,176,174],0.109,[128,181,128,345,176,174,176,176,174],0.116,[128,128,128,347,176,174,176,176,174],0.645,[210,174,128,349,176,174,176,176,174],0.105,[210,178,128,351,176,174,176,176,174],0.129,[210,181,128,353,176,174,176,176,174],0.117,[210,128,128,355,176,174,176,176,174],0.665,[219,174,128,224,176,174,176,176,174],[219,178,128,358,176,174,176,176,174],0.124,[219,181,128,360,176,174,176,176,174],0.113,[219,128,128,362,176,174,176,176,174],0.681,[],[133],[],[],[368],"Oxford Spires surface reconstruction against the millimetre-accurate Leica RTC360 TLS reference map; localization disabled and ground-truth poses used for all methods; OpenMVS and Nerfacto results taken from the benchmark (offline batch); meshes at 0.1 m resolution; F-score threshold 0.1 m; precision and recall columns not extracted",{"slug":370,"group":371,"sourceId":5,"sourceLabel":6,"table":372,"selfRows":373,"metrics":374,"seqs":382,"entrants":388,"cells":407,"outcomes":471,"locators":472,"hardware":473,"wordings":474,"notes":475},"pings2025-table-iii","pings2025:Table III","Table III",8,[375,380],{"label":376,"unit":377,"statistic":378,"alignment":379},"ARTE [%] (average relative translation error)","%","mean","none",{"label":381,"unit":138,"statistic":41,"alignment":41},"ATE [m] (absolute trajectory error; statistic not stated)",[383,386],{"dataset":84,"sequence":384,"environment":385},"Seq. 1 (5.0 km)","outdoor urban driving (robot car)",{"dataset":84,"sequence":387,"environment":385},"Seq. 2 (3.7 km)",[389,392,395,397,399,402,405,406],{"name":390,"methodId":391,"linkable":166,"proposed":75,"self":75},"F-LOAM [69]","floam2021",{"name":393,"methodId":394,"linkable":166,"proposed":75,"self":75},"KISS-ICP [68]","kissicp2023",{"name":396,"methodId":169,"linkable":166,"proposed":75,"self":75},"PIN odometry [51]",{"name":398,"methodId":5,"linkable":166,"proposed":166,"self":166},"PINGS odometry",{"name":400,"methodId":401,"linkable":166,"proposed":75,"self":75},"SuMa [3]","suma2018",{"name":403,"methodId":404,"linkable":166,"proposed":75,"self":75},"MULLS [50]","mulls2021",{"name":168,"methodId":169,"linkable":166,"proposed":75,"self":75},{"name":171,"methodId":5,"linkable":166,"proposed":166,"self":166},[408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,461,463,465,467,469],[174,174,174,409,176,174,176,176,174],1.96,[174,178,174,411,176,174,176,176,174],28.52,[174,174,178,413,176,174,176,176,174],1.93,[174,178,178,415,176,174,176,176,174],27,[178,174,174,417,176,174,176,176,174],1.49,[178,178,174,419,176,174,176,176,174],8.17,[178,174,178,421,176,174,176,176,174],1.38,[178,178,178,423,176,174,176,176,174],8.22,[181,174,174,425,176,174,176,176,174],0.95,[181,178,174,427,176,174,176,176,174],4.51,[181,174,178,429,176,174,176,176,174],0.98,[181,178,178,431,176,174,176,176,174],5.64,[128,174,174,433,176,174,176,176,174],0.73,[128,178,174,435,176,174,176,176,174],5.17,[128,174,178,437,176,174,176,176,174],0.59,[128,178,178,439,176,174,176,176,174],4.78,[210,174,174,441,176,174,176,176,174],5.55,[210,178,174,443,176,174,176,176,174],39.9,[210,174,178,445,176,174,176,176,174],4.42,[210,178,178,447,176,174,176,176,174],44.78,[219,174,174,449,176,174,176,176,174],2.23,[219,178,174,451,176,174,176,176,174],40.37,[219,174,178,453,176,174,176,176,174],1.64,[219,178,178,455,176,174,176,176,174],33.82,[457,174,174,178,176,174,176,176,174],6,[457,178,174,459,176,174,176,176,174],3.17,[457,174,178,429,176,174,176,176,174],[457,178,178,462,176,174,176,176,174],4.44,[464,174,174,288,176,174,176,176,174],7,[464,178,174,466,176,174,176,176,174],1.99,[464,174,178,468,176,174,176,176,174],0.58,[464,178,178,470,176,174,176,176,174],3.47,[],[372],[],[],[476],"In-house car dataset (Bonn), full sequences; reference poses from offline LiDAR bundle adjustment with RTK-GNSS, point cloud alignment and geo-referenced TLS constraints; odometry methods above, SLAM methods below; ATE alignment not stated",{"slug":478,"group":479,"sourceId":5,"sourceLabel":6,"table":480,"selfRows":178,"metrics":481,"seqs":485,"entrants":488,"cells":490,"outcomes":492,"locators":493,"hardware":495,"wordings":497,"notes":498},"pings2025-text-sec-v","pings2025:Text Sec. V","Text Sec. V",[482],{"label":483,"unit":484,"statistic":41,"alignment":73},"overall processing time of around five seconds per frame","s",[486],{"dataset":487,"sequence":73,"environment":73},"in-house car dataset and Oxford Spires (not specified)",[489],{"name":171,"methodId":5,"linkable":166,"proposed":166,"self":166},[491],[174,174,174,219,176,174,174,176,174],[],[494],"Sec. V Limitations",[496],"single NVIDIA A6000 GPU",[],[499],"Overall processing time stated in the limitations; SDF mapping and LiDAR odometry run at sensor frame rate, radiance-field mapping dominates",[],1790510659408]