{"id":"https://openalex.org/W7163386084","doi":"https://doi.org/10.48550/arxiv.2606.03905","title":"Semantic-weighted ICP for LiDAR Odometry: Class-Aware Residual Reweighting for Robust Scan Registration","display_name":"Semantic-weighted ICP for LiDAR Odometry: Class-Aware Residual Reweighting for Robust Scan Registration","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163386084","doi":"https://doi.org/10.48550/arxiv.2606.03905"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03905","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03905","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.03905","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137722710","display_name":"Vasco Carvalho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carvalho, Vasco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137722577","display_name":"Tiago Barros","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barros, Tiago","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5011728288","display_name":"Urbano Nunes","orcid":"https://orcid.org/0000-0002-7750-5221"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nunes, Urbano J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7293999791145325},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6377999782562256},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6200000047683716},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5873000025749207},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5209000110626221},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49970000982284546},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.43479999899864197},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42879998683929443}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7293999791145325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.663100004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6398000121116638},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6377999782562256},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5358999967575073},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5209000110626221},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.3862000107765198},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.3158999979496002},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.30399999022483826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C136520226","wikidata":"https://www.wikidata.org/wiki/Q302814","display_name":"Geometric data analysis","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C195958017","wikidata":"https://www.wikidata.org/wiki/Q1675268","display_name":"Iterative closest point","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2773999869823456},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03905","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03905","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03905","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03905","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7697820663452148,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LiDAR":[0,100],"odometry":[1],"is":[2,205],"a":[3,95],"fundamental":[4],"component":[5],"of":[6,54,103,119,149,201,215],"autonomous":[7],"robotic":[8],"systems,":[9],"relying":[10],"on":[11,76,126,157],"geometric":[12,23,41,129],"registration":[13,69,143],"between":[14],"consecutive":[15],"point":[16],"clouds":[17],"to":[18,33,109,122,139,141],"estimate":[19],"ego-motion.":[20],"However,":[21],"traditional":[22],"approaches":[24],"often":[25],"degrade":[26],"in":[27,67,84,184],"dynamic":[28,150],"or":[29],"unstructured":[30],"environments":[31],"due":[32],"unreliable":[34],"correspondences":[35],"caused":[36],"by":[37,60,209],"moving":[38],"objects,":[39],"sparse":[40],"features,":[42],"vegetation,":[43],"and":[44,78,160,168,212],"semantically":[45],"ambiguous":[46],"structures.":[47],"Existing":[48],"works":[49],"have":[50],"shown":[51],"that,":[52],"some":[53],"these":[55],"limitations":[56],"can":[57],"be":[58],"addressed":[59],"introducing":[61],"semantic":[62,96,111,123,213],"information":[63],"from":[64],"the":[65,68,85,113,117,142,147,158,176,195,199,210,216],"environment":[66,86],"process.":[70],"In":[71],"this":[72,202],"work,":[73],"we":[74,93],"build":[75],"this,":[77],"show":[79,174],"that":[80,175,198],"not":[81],"all":[82],"elements":[83],"are":[87,192],"equally":[88],"relevant":[89],"for":[90,99],"registration.":[91],"Hence,":[92],"propose":[94],"class-weighted":[97],"ICP":[98,179],"odometry.":[101],"Instead":[102],"strictly":[104],"filtering":[105],"out":[106],"points":[107,120],"belonging":[108,121],"specific":[110],"classes,":[112],"proposed":[114,177],"approach":[115],"weights":[116],"residuals":[118],"categories":[124],"based":[125],"their":[127],"expected":[128],"stability.":[130],"This":[131],"strategy":[132,204],"enables":[133],"informative":[134],"but":[135],"potentially":[136],"unstable":[137],"structures,":[138],"contribute":[140],"process":[144],"while":[145],"mitigating":[146],"influence":[148],"objects.":[151],"The":[152,171],"experimental":[153],"evaluation":[154],"was":[155],"conducted":[156],"SemanticKITTI":[159],"RELLIS-3D":[161],"datasets,":[162],"which":[163],"include":[164],"urban,":[165],"highway,":[166],"rural,":[167],"off-road":[169,186],"environments.":[170],"empirical":[172],"results":[173],"Semantic-weighted":[178],"improves":[180],"pose":[181],"estimation,":[182],"especially":[183],"challenging":[185],"scenarios":[187],"where":[188],"conventional":[189],"rigid":[190],"features":[191],"scarce.":[193],"Furthermore,":[194],"analysis":[196],"reveals":[197],"effectiveness":[200],"weighting":[203],"highly":[206],"environment-dependent,":[207],"influenced":[208],"structural":[211],"composition":[214],"scene.":[217]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}