{"id":"https://openalex.org/W7161753027","doi":"https://doi.org/10.1109/icarsc70216.2026.11523258","title":"Proactive Feature Quality Assessment for LiDAR-Inertial Odometry Using Physical Signal Analysis","display_name":"Proactive Feature Quality Assessment for LiDAR-Inertial Odometry Using Physical Signal Analysis","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7161753027","doi":"https://doi.org/10.1109/icarsc70216.2026.11523258"},"language":null,"primary_location":{"id":"doi:10.1109/icarsc70216.2026.11523258","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarsc70216.2026.11523258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136514508","display_name":"Niusha Khosravi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niusha Khosravi","raw_affiliation_strings":["Instituto Superior T&#x00E9;cnico,Institute for Systems and Robotics,Lisbon,Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Superior T&#x00E9;cnico,Institute for Systems and Robotics,Lisbon,Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052413681","display_name":"Rodrigo Ventura","orcid":"https://orcid.org/0000-0002-5655-9562"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rodrigo Ventura","raw_affiliation_strings":["Instituto Superior T&#x00E9;cnico,Institute for Systems and Robotics,Lisbon,Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Superior T&#x00E9;cnico,Institute for Systems and Robotics,Lisbon,Portugal","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046287015","display_name":"Meysam Basiri","orcid":"https://orcid.org/0000-0002-8456-6284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meysam Basiri","raw_affiliation_strings":["Instituto Superior T&#x00E9;cnico,Institute for Systems and Robotics,Lisbon,Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Superior T&#x00E9;cnico,Institute for Systems and Robotics,Lisbon,Portugal","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.58895561,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"138","last_page":"144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9315999746322632,"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":0.9315999746322632,"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.020999999716877937,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.007000000216066837,"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/feature","display_name":"Feature (linguistics)","score":0.5246000289916992},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4320000112056732},{"id":"https://openalex.org/keywords/quality-assessment","display_name":"Quality assessment","score":0.39500001072883606},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.3767000138759613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3653999865055084}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6237999796867371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5995000004768372},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5177000164985657},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.39500001072883606},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34540000557899475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.2922999858856201}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarsc70216.2026.11523258","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarsc70216.2026.11523258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2048312361","https://openalex.org/W2296228853","https://openalex.org/W2745007981","https://openalex.org/W3003991701","https://openalex.org/W3091486995","https://openalex.org/W3129245057","https://openalex.org/W3133648073","https://openalex.org/W3199184510","https://openalex.org/W3203066970","https://openalex.org/W4210423514","https://openalex.org/W4285269741","https://openalex.org/W4292544413","https://openalex.org/W4312239696","https://openalex.org/W4313361257","https://openalex.org/W4315778466","https://openalex.org/W4383109468","https://openalex.org/W4391270188","https://openalex.org/W4401416488","https://openalex.org/W4403977964","https://openalex.org/W4404356568","https://openalex.org/W4409919465","https://openalex.org/W4416747759","https://openalex.org/W4416749892","https://openalex.org/W4417130892"],"related_works":[],"abstract_inverted_index":{"Most":[0],"traditional":[1],"LiDAR-Inertial":[2],"Odometry":[3],"(LIO)":[4],"frameworks":[5],"rely":[6],"on":[7,139,144],"uniform":[8],"measurement":[9,76,114,205],"noise":[10],"models,":[11],"which":[12],"can":[13],"limit":[14],"robustness":[15,213],"in":[16,182,214],"geometrically":[17],"degenerate":[18],"or":[19],"densely":[20],"cluttered":[21],"environments":[22],"characterized":[23],"by":[24,161],"varying":[25],"signal":[26,83],"reliability.":[27],"Although":[28],"adaptive":[29],"methods":[30],"exist":[31],"to":[32,164],"mitigate":[33],"this":[34,64,186],"issue,":[35],"they":[36,58],"are":[37,59,104],"predominantly":[38],"reactive,":[39],"adjusting":[40],"covariance":[41,115],"only":[42,167],"after":[43],"analyzing":[44],"optimization":[45],"residuals.":[46],"Consequently,":[47],"unreliable":[48],"measurements":[49],"may":[50],"already":[51],"have":[52],"affected":[53],"the":[54,113,117,137,140,151,155,172,175,199],"state":[55],"estimate":[56],"before":[57],"identified":[60],"and":[61,99,143],"downweighted.":[62],"In":[63],"work,":[65],"we":[66],"propose":[67],"a":[68,78,107,145,208],"proactive":[69],"feature":[70],"quality":[71],"assessment":[72],"framework":[73,125],"that":[74,111,202],"estimates":[75],"reliability":[77],"priori":[79],"using":[80],"three":[81],"physical":[82],"cues:":[84],"photometric":[85],"stability,":[86],"quantified":[87],"through":[88],"local":[89],"intensity":[90],"variance;":[91],"geometric":[92],"planarity,":[93],"derived":[94],"from":[95],"eigenvalue-based":[96],"structure":[97],"analysis;":[98],"range-dependent":[100],"uncertainty.":[101],"These":[102],"cues":[103],"fused":[105],"into":[106],"per-point":[108],"trust":[109],"score":[110],"modulates":[112],"within":[116],"FAST-LIO2":[118],"Iterated":[119],"Extended":[120],"Kalman":[121],"Filter":[122],"(IEKF).":[123],"The":[124],"uses":[126],"physically":[127],"interpretable":[128],"parameters,":[129],"although":[130,185],"parameter":[131],"selection":[132],"remains":[133,189],"sensor-dependent.":[134],"We":[135],"evaluate":[136],"method":[138,157,176],"MARS-LVIG":[141,152],"benchmark":[142],"custom":[146],"aerial":[147,216],"golf-course":[148,173],"dataset.":[149],"On":[150,171],"featureless":[153],"sequence,":[154],"proposed":[156],"reduces":[158],"trajectory":[159],"error":[160],"63.1%":[162],"relative":[163],"FAST-LIO2,":[165],"with":[166],"4%":[168],"runtime":[169],"overhead.":[170],"dataset,":[174],"yields":[177],"qualitatively":[178],"improved":[179],"map":[180],"consistency":[181],"vegetation-dominated":[183],"regions,":[184],"second":[187],"evaluation":[188],"preliminary":[190],"because":[191],"RTK-GPS":[192],"ground":[193],"truth":[194],"was":[195],"not":[196],"available.":[197],"Overall,":[198],"results":[200],"indicate":[201],"proactive,":[203],"physics-informed":[204],"weighting":[206],"is":[207],"promising":[209],"direction":[210],"for":[211],"improving":[212],"challenging":[215],"LiDAR":[217],"environments.":[218]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-21T00:00:00"}
