{"id":"https://openalex.org/W4405785156","doi":"https://doi.org/10.1109/iros58592.2024.10802379","title":"A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM","display_name":"A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4405785156","doi":"https://doi.org/10.1109/iros58592.2024.10802379"},"language":"en","primary_location":{"id":"doi:10.1109/iros58592.2024.10802379","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5018307186","display_name":"Yasra Chandio","orcid":"https://orcid.org/0000-0002-3436-6452"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yasra Chandio","raw_affiliation_strings":["University of Massachusetts,Amherst,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts,Amherst,USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044260416","display_name":"Momin Ahmad Khan","orcid":"https://orcid.org/0000-0002-6009-514X"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Momin A. Khan","raw_affiliation_strings":["University of Massachusetts,Amherst,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts,Amherst,USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000633424","display_name":"Khotso Selialia","orcid":"https://orcid.org/0000-0002-0710-6794"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khotso Selialia","raw_affiliation_strings":["University of Massachusetts,Amherst,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts,Amherst,USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057161031","display_name":"Luis Miguel S\u00e1nchez Garc\u00eda","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luis Garcia","raw_affiliation_strings":["University of Utah,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah,USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059704918","display_name":"Joseph DeGol","orcid":"https://orcid.org/0009-0006-7950-0666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joseph DeGol","raw_affiliation_strings":["Steg AI,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Steg AI,USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038455798","display_name":"Fatima M. Anwar","orcid":"https://orcid.org/0000-0001-7119-7232"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fatima M. Anwar","raw_affiliation_strings":["University of Massachusetts,Amherst,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts,Amherst,USA","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4941","last_page":"4948"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.987500011920929,"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":0.987500011920929,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9722999930381775,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9330000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6987788677215576},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6751867532730103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5902130603790283},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.47619831562042236},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4610949754714966},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4426066279411316},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4076474905014038},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07069516181945801}],"concepts":[{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6987788677215576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6751867532730103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5902130603790283},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.47619831562042236},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4610949754714966},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4426066279411316},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4076474905014038},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07069516181945801},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros58592.2024.10802379","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1491719799","https://openalex.org/W1964423847","https://openalex.org/W2014596857","https://openalex.org/W2115579991","https://openalex.org/W2117228865","https://openalex.org/W2117769210","https://openalex.org/W2125501610","https://openalex.org/W2151103935","https://openalex.org/W2151290401","https://openalex.org/W2170288257","https://openalex.org/W2295283246","https://openalex.org/W2396274919","https://openalex.org/W2461937780","https://openalex.org/W2789469977","https://openalex.org/W2946547492","https://openalex.org/W3035056458","https://openalex.org/W3043971245","https://openalex.org/W3045702834","https://openalex.org/W3131713303","https://openalex.org/W3194668998","https://openalex.org/W3200702630","https://openalex.org/W3214664069","https://openalex.org/W4206434639","https://openalex.org/W4206647573","https://openalex.org/W4210951069","https://openalex.org/W4214873631","https://openalex.org/W4226208169","https://openalex.org/W4293225101","https://openalex.org/W4297996359","https://openalex.org/W4317926971","https://openalex.org/W4377832123","https://openalex.org/W4378830682","https://openalex.org/W4389665609","https://openalex.org/W6600324250","https://openalex.org/W6729482287","https://openalex.org/W6745272545","https://openalex.org/W6754944153","https://openalex.org/W6757902542","https://openalex.org/W6780901898","https://openalex.org/W6852774513","https://openalex.org/W6859475906"],"related_works":["https://openalex.org/W2601157893","https://openalex.org/W2373006798","https://openalex.org/W2131735617","https://openalex.org/W2056912418","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2049538278","https://openalex.org/W2886173746","https://openalex.org/W4200043248"],"abstract_inverted_index":{"Autonomous":[0],"robots,":[1],"autonomous":[2],"vehicles,":[3],"and":[4,11,32,42,70,147,205,211,221],"humans":[5],"wearing":[6],"mixed-reality":[7],"headsets":[8],"require":[9],"accurate":[10],"reliable":[12],"tracking":[13,26],"services":[14],"for":[15,68,144,198],"safety-critical":[16],"applications":[17],"in":[18,74],"dynamically":[19],"changing":[20],"real-world":[21,149],"environments.":[22,225],"However,":[23],"the":[24,50,86,98,113,124,141,148,173,194,199,218],"existing":[25],"approaches,":[27],"such":[28],"as":[29],"Simultaneous":[30],"Localization":[31],"Mapping":[33],"(SLAM),":[34],"do":[35],"not":[36],"adapt":[37,59],"well":[38],"to":[39,60,76,91,108,171,209,213,223],"environmental":[40,61],"changes":[41],"boundary":[43],"conditions":[44],"despite":[45],"extensive":[46],"manual":[47],"tuning.":[48],"On":[49],"other":[51],"hand,":[52],"while":[53,105,167],"deep":[54],"learning-based":[55],"approaches":[56,104],"can":[57,115,136],"better":[58],"changes,":[62],"they":[63],"typically":[64],"demand":[65],"substantial":[66],"data":[67,107],"training":[69],"often":[71],"lack":[72],"flexibility":[73],"adapting":[75],"new":[77],"domains.":[78],"To":[79],"solve":[80],"this":[81],"problem,":[82],"we":[83,120],"propose":[84],"leveraging":[85,106],"neurosymbolic":[87,156,184],"program":[88],"synthesis":[89],"approach":[90,114],"construct":[92],"adaptable":[93],"SLAM":[94,103,118],"pipelines":[95],"that":[96,135,181],"integrate":[97],"domain":[99,138],"knowledge":[100,139],"from":[101],"traditional":[102],"learn":[109],"complex":[110],"relationships.":[111],"While":[112],"synthesize":[116],"end-to-end":[117],"pipelines,":[119],"focus":[121],"on":[122,140],"synthesizing":[123],"feature":[125,145,153,161,176,202],"extraction":[126,146],"module.":[127],"We":[128],"first":[129],"devise":[130],"a":[131],"domain-specific":[132],"language":[133],"(DSL)":[134],"encapsulate":[137],"essential":[142],"attributes":[143],"performance":[150],"of":[151],"various":[152],"extractors.":[154],"Our":[155,178],"architecture":[157],"then":[158],"undertakes":[159],"adaptive":[160],"extraction,":[162],"optimizing":[163],"parameters":[164],"via":[165],"learning":[166],"employing":[168],"symbolic":[169],"reasoning":[170],"select":[172],"most":[174],"suitable":[175],"extractor.":[177],"evaluations":[179],"demonstrate":[180],"our":[182],"approach,":[183],"Feature":[185],"EXtraction":[186],"(nFEX),":[187],"yields":[188],"higher-quality":[189],"features.":[190],"It":[191],"also":[192],"reduces":[193],"pose":[195],"error":[196],"observed":[197],"state-of-the-art":[200],"baseline":[201],"extractors":[203],"ORB":[204],"SIFT":[206],"by":[207],"up":[208,212],"90%":[210],"66%,":[214],"respectively,":[215],"thereby":[216],"enhancing":[217],"system\u2019s":[219],"efficiency":[220],"adaptability":[222],"novel":[224]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
