{"id":"https://openalex.org/W4322731634","doi":"https://doi.org/10.1109/iccp56966.2022.10053981","title":"Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods","display_name":"Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods","publication_year":2022,"publication_date":"2022-09-22","ids":{"openalex":"https://openalex.org/W4322731634","doi":"https://doi.org/10.1109/iccp56966.2022.10053981"},"language":"en","primary_location":{"id":"doi:10.1109/iccp56966.2022.10053981","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iccp56966.2022.10053981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)","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/A5067365630","display_name":"Corvin-Petru\u021b Cob\u00e2rzan","orcid":null},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Corvin-Petru\u021b Cob\u00e2rzan","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Department,Romania","Computer Science Department, Technical University of Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Department,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Department, Technical University of Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079505023","display_name":"C\u0103t\u0103lin-Cosmin Golban","orcid":null},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"C\u0103t\u0103lin-Cosmin Golban","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Department,Romania","Computer Science Department, Technical University of Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Department,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Department, Technical University of Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047954457","display_name":"Sergiu Nedevschi","orcid":"https://orcid.org/0000-0003-2018-4647"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Sergiu Nedevschi","raw_affiliation_strings":["Technical University of Cluj-Napoca,Image Processing and Pattern Recognition Group,Romania","Image Processing and Pattern Recognition Group, Technical University of Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Image Processing and Pattern Recognition Group,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Image Processing and Pattern Recognition Group, Technical University of Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":0.0924,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44181143,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"abs/1704.05519","issue":null,"first_page":"183","last_page":"188"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998000264167786,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9988999962806702,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9864000082015991,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6735836863517761},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6728195548057556},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6604031920433044},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5828080177307129},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.5825963020324707},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.547950267791748},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5044320821762085},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4830968379974365},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.477098286151886},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4624932110309601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44458621740341187},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1120871901512146},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.10643073916435242},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.08665475249290466},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07808142900466919},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.07318460941314697},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.06901538372039795}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6735836863517761},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6728195548057556},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6604031920433044},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5828080177307129},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.5825963020324707},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.547950267791748},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5044320821762085},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4830968379974365},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.477098286151886},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4624932110309601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44458621740341187},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1120871901512146},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.10643073916435242},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.08665475249290466},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07808142900466919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.07318460941314697},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.06901538372039795},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccp56966.2022.10053981","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iccp56966.2022.10053981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1512698229","https://openalex.org/W1588720062","https://openalex.org/W1612997784","https://openalex.org/W1617689420","https://openalex.org/W2015996585","https://openalex.org/W2031998160","https://openalex.org/W2068289541","https://openalex.org/W2097197771","https://openalex.org/W2124313187","https://openalex.org/W2137666905","https://openalex.org/W2149109100","https://openalex.org/W2150066425","https://openalex.org/W2160823029","https://openalex.org/W2161779787","https://openalex.org/W2165284796","https://openalex.org/W2166132830","https://openalex.org/W2296228853","https://openalex.org/W2544243493","https://openalex.org/W2891899936","https://openalex.org/W2948440099","https://openalex.org/W2961764720","https://openalex.org/W3108490186","https://openalex.org/W3149884368","https://openalex.org/W4243425824","https://openalex.org/W4247696166","https://openalex.org/W4300124531","https://openalex.org/W4302337790","https://openalex.org/W6737309142","https://openalex.org/W6750958682","https://openalex.org/W6762768537"],"related_works":["https://openalex.org/W2979950214","https://openalex.org/W87609089","https://openalex.org/W3024737167","https://openalex.org/W2414561716","https://openalex.org/W3161199934","https://openalex.org/W2303855011","https://openalex.org/W2312326526","https://openalex.org/W3105866016","https://openalex.org/W2412578866","https://openalex.org/W4312703710"],"abstract_inverted_index":{"We":[0,74],"have":[1],"previously":[2,58],"proposed":[3],"a":[4,13,50],"linear":[5,64],"approach":[6],"for":[7],"reducing":[8],"the":[9,29,45,56,71,85],"global":[10],"drift":[11],"of":[12,31,49,52,87],"video-based":[14],"frame-to-frame":[15],"trajectory":[16],"estimation":[17,97],"method":[18,89],"by":[19],"correcting":[20],"it":[21],"at":[22],"selected":[23],"points":[24],"in":[25,92],"time":[26,61],"based":[27],"on":[28],"alignment":[30],"past":[32],"and":[33,65,83],"current":[34],"3D":[35],"LiDAR":[36],"measurements":[37],"(see":[38],"[7]).":[39],"In":[40],"this":[41,60],"paper":[42],"we":[43],"assess":[44,84],"tolerance":[46],"to":[47,69],"noise":[48,80],"series":[51],"methods":[53,68],"derived":[54],"from":[55],"one":[57],"proposed,":[59],"using":[62],"both":[63],"non-linear":[66],"optimization":[67],"calculate":[70],"correction":[72],"transform.":[73],"generate":[75],"synthetic":[76],"datasets":[77],"with":[78],"various":[79],"pollution":[81],"levels":[82],"performance":[86],"each":[88],"under":[90],"investigation":[91],"recovering":[93],"artificially":[94],"induced":[95],"odometry":[96],"errors.":[98]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}