{"id":"https://openalex.org/W3199331947","doi":"https://doi.org/10.1109/iv51971.2022.9827133","title":"ROOAD: RELLIS Off-road Odometry Analysis Dataset","display_name":"ROOAD: RELLIS Off-road Odometry Analysis Dataset","publication_year":2022,"publication_date":"2022-06-05","ids":{"openalex":"https://openalex.org/W3199331947","doi":"https://doi.org/10.1109/iv51971.2022.9827133","mag":"3199331947"},"language":"en","primary_location":{"id":"doi:10.1109/iv51971.2022.9827133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827133","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"article","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/A5011055644","display_name":"George Chustz","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164117","display_name":"Walker (United States)","ror":"https://ror.org/05hgh7849","country_code":"US","type":"company","lineage":["https://openalex.org/I4210164117"]},{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Chustz","raw_affiliation_strings":["Texas A&#x0026;M University,J. Mike Walker &#x2019;66 Department of Mechanical Engineering,College Station,TX,USA,77843"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&#x0026;M University,J. Mike Walker &#x2019;66 Department of Mechanical Engineering,College Station,TX,USA,77843","institution_ids":["https://openalex.org/I91045830","https://openalex.org/I4210164117"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053144325","display_name":"Srikanth Saripalli","orcid":"https://orcid.org/0000-0002-3906-7574"},"institutions":[{"id":"https://openalex.org/I4210164117","display_name":"Walker (United States)","ror":"https://ror.org/05hgh7849","country_code":"US","type":"company","lineage":["https://openalex.org/I4210164117"]},{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srikanth Saripalli","raw_affiliation_strings":["Texas A&#x0026;M University,J. Mike Walker &#x2019;66 Department of Mechanical Engineering,College Station,TX,USA,77843"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&#x0026;M University,J. Mike Walker &#x2019;66 Department of Mechanical Engineering,College Station,TX,USA,77843","institution_ids":["https://openalex.org/I91045830","https://openalex.org/I4210164117"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6172,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91282225,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9986000061035156,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9968000054359436,"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/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.8076741695404053},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8015004396438599},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.7724156379699707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7638452053070068},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7052404880523682},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.6294832229614258},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5312297940254211},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5123165845870972},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.4703633487224579},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.46691444516181946},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.46492862701416016},{"id":"https://openalex.org/keywords/bundle-adjustment","display_name":"Bundle adjustment","score":0.43276649713516235},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.43173953890800476},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.2567277252674103},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.12845304608345032},{"id":"https://openalex.org/keywords/photogrammetry","display_name":"Photogrammetry","score":0.09411263465881348},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08660626411437988}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.8076741695404053},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015004396438599},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.7724156379699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7638452053070068},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7052404880523682},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.6294832229614258},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5312297940254211},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5123165845870972},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.4703633487224579},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.46691444516181946},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.46492862701416016},{"id":"https://openalex.org/C179458375","wikidata":"https://www.wikidata.org/wiki/Q1020763","display_name":"Bundle adjustment","level":3,"score":0.43276649713516235},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.43173953890800476},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2567277252674103},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.12845304608345032},{"id":"https://openalex.org/C117455697","wikidata":"https://www.wikidata.org/wiki/Q190149","display_name":"Photogrammetry","level":2,"score":0.09411263465881348},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08660626411437988},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv51971.2022.9827133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827133","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6600000262260437,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1488888263","https://openalex.org/W2021851106","https://openalex.org/W2091790851","https://openalex.org/W2115579991","https://openalex.org/W2340897893","https://openalex.org/W2396274919","https://openalex.org/W2537902178","https://openalex.org/W2600777408","https://openalex.org/W2746505796","https://openalex.org/W2754177129","https://openalex.org/W2765757095","https://openalex.org/W2781228439","https://openalex.org/W2798513908","https://openalex.org/W2909063396","https://openalex.org/W2909157769","https://openalex.org/W2962987986","https://openalex.org/W2963813675","https://openalex.org/W2968243907","https://openalex.org/W3003922739","https://openalex.org/W3011788244","https://openalex.org/W3014641072","https://openalex.org/W3014771270","https://openalex.org/W3016101116","https://openalex.org/W3016404858","https://openalex.org/W3035574168","https://openalex.org/W3091667825","https://openalex.org/W3109808990","https://openalex.org/W3132270109","https://openalex.org/W3206164009","https://openalex.org/W4254843666","https://openalex.org/W4394659040","https://openalex.org/W6735560773","https://openalex.org/W6767071366"],"related_works":["https://openalex.org/W3090894413","https://openalex.org/W2003315864","https://openalex.org/W2963656298","https://openalex.org/W2601655733","https://openalex.org/W2971051170","https://openalex.org/W3004363006","https://openalex.org/W3162073984","https://openalex.org/W2980356813","https://openalex.org/W197633916","https://openalex.org/W3121134787"],"abstract_inverted_index":{"The":[0],"development":[1,46],"and":[2,61,93,145,152,154,171,186],"implementation":[3],"of":[4,47,180,201],"visual-inertial":[5,40],"odometry":[6],"(VIO)":[7],"has":[8,89],"focused":[9],"on":[10,54,76],"structured":[11,85],"environments,":[12],"but":[13],"interest":[14],"in":[15,17,84,98,106,109,124,164,197,203],"localization":[16,189],"off-road":[18,38,100,204],"environments":[19],"is":[20,121],"growing.":[21],"In":[22],"this":[23,125],"paper,":[24],"we":[25,127],"present":[26],"the":[27,45,52,77,99,114,165,169,198],"RELLIS":[28],"Off-road":[29],"Odometry":[30],"Analysis":[31],"Dataset":[32],"(ROOAD)":[33],"which":[34],"provides":[35,176],"high-quality,":[36],"time-synchronized":[37],"monocular":[39],"data":[41,111,132],"sequences":[42],"to":[43,72,81,184],"further":[44],"related":[48],"research.":[49],"We":[50],"evaluated":[51],"dataset":[53,79,175],"two":[55],"state-of-the-art":[56],"VIO":[57,202],"algorithms,":[58],"(1)":[59],"Open-VINS":[60],"(2)":[62],"VINS-Fusion.":[63],"Our":[64,134],"findings":[65],"indicate":[66],"that":[67],"both":[68],"algorithms":[69,190],"perform":[70],"2":[71],"30":[73],"times":[74],"worse":[75,160],"ROOAD":[78,206],"compared":[80],"their":[82,188],"performance":[83,95,200],"environments.":[86,205],"Furthermore,":[87],"OpenVINS":[88,105],"better":[90],"tracking":[91,107],"stability":[92],"real-time":[94],"than":[96],"VINS-Fusion":[97,103],"environment,":[101],"while":[102],"outperformed":[104],"accuracy":[108],"several":[110,130],"sequences.":[112,133],"Since":[113],"camera-IMU":[115],"calibration":[116,131],"tool":[117,139],"from":[118],"Kalibr":[119],"toolkit":[120],"used":[122],"extensively":[123],"work,":[126],"have":[128],"included":[129],"hand":[135],"measurements":[136],"show":[137],"Kalibr\u2019s":[138],"achieved":[140],"\u00b11\u00b0":[141],"for":[142,161,182],"orientation":[143],"error":[144,163],"\u00b1":[146,155],"1":[147],"mm":[148,157],"at":[149,159],"best":[150],"(x-":[151],"y-axis)":[153],"10":[156],"(z-axis)":[158],"position":[162],"camera":[166,170],"frame":[167],"between":[168],"IMU.":[172],"This":[173],"novel":[174],"a":[177],"new":[178],"set":[179],"scenarios":[181],"researchers":[183],"design":[185],"test":[187],"on,":[191],"as":[192,194],"well":[193],"critical":[195],"insights":[196],"current":[199],"Dataset:":[207],"github.com/unmannedlab/ROOAD":[208]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
