{"id":"https://openalex.org/W2530906228","doi":"https://doi.org/10.1109/iros.2016.7758089","title":"Low-latency visual odometry using event-based feature tracks","display_name":"Low-latency visual odometry using event-based feature tracks","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2530906228","doi":"https://doi.org/10.1109/iros.2016.7758089","mag":"2530906228"},"language":"en","primary_location":{"id":"doi:10.1109/iros.2016.7758089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2016.7758089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069504180","display_name":"Beat Kueng","orcid":null},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Beat Kueng","raw_affiliation_strings":["Robotics and Perception Group, University of Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"Robotics and Perception Group, University of Zurich, Switzerland","institution_ids":["https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083488340","display_name":"Elias Mueggler","orcid":"https://orcid.org/0000-0002-8008-443X"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Elias Mueggler","raw_affiliation_strings":["Robotics and Perception Group, University of Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"Robotics and Perception Group, University of Zurich, Switzerland","institution_ids":["https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068567652","display_name":"Guillermo Gallego","orcid":"https://orcid.org/0000-0002-2672-9241"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Guillermo Gallego","raw_affiliation_strings":["Robotics and Perception Group, University of Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"Robotics and Perception Group, University of Zurich, Switzerland","institution_ids":["https://openalex.org/I202697423"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057116316","display_name":"Davide Scaramuzza","orcid":"https://orcid.org/0000-0002-3831-6778"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Davide Scaramuzza","raw_affiliation_strings":["Robotics and Perception Group, University of Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"Robotics and Perception Group, University of Zurich, Switzerland","institution_ids":["https://openalex.org/I202697423"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069504180"],"corresponding_institution_ids":["https://openalex.org/I202697423"],"apc_list":null,"apc_paid":null,"fwci":286.8319,"has_fulltext":false,"cited_by_count":195,"citation_normalized_percentile":{"value":0.99979688,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"23"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9962000250816345,"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/computer-science","display_name":"Computer science","score":0.7501132488250732},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.7331593632698059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7171154022216797},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6486310958862305},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.546891987323761},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5131662487983704},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.48708534240722656},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.43435797095298767},{"id":"https://openalex.org/keywords/low-latency","display_name":"Low latency (capital markets)","score":0.42101195454597473},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.15731653571128845},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.12549284100532532},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.058180391788482666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7501132488250732},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.7331593632698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7171154022216797},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6486310958862305},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.546891987323761},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5131662487983704},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.48708534240722656},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.43435797095298767},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.42101195454597473},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.15731653571128845},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.12549284100532532},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.058180391788482666},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/iros.2016.7758089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2016.7758089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},{"id":"pmh:oai:www.zora.uzh.ch:125437","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Kueng, Beat; M\u00fcggler, Elias; Gallego, Guillermo; Scaramuzza, Davide  (2016). Low-latency visual odometry using event-based feature tracks.  In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 9 October 2016 - 14 October 2016. Institute of Electrical and Electronics Engineers (IEEE), 1-8.","raw_type":"Conference or Workshop Item"},{"id":"pmh:oai:infoscience.epfl.ch:220001","is_oa":false,"landing_page_url":"http://infoscience.epfl.ch/record/220001","pdf_url":null,"source":{"id":"https://openalex.org/S4306400488","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"doi:10.5167/uzh-125437","is_oa":true,"landing_page_url":"https://doi.org/10.5167/uzh-125437","pdf_url":null,"source":{"id":"https://openalex.org/S7407051291","display_name":"Universit\u00e4t Z\u00fcrich, ZORA","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":""}],"best_oa_location":{"id":"pmh:oai:www.zora.uzh.ch:125437","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Kueng, Beat; M\u00fcggler, Elias; Gallego, Guillermo; Scaramuzza, Davide  (2016). Low-latency visual odometry using event-based feature tracks.  In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 9 October 2016 - 14 October 2016. Institute of Electrical and Electronics Engineers (IEEE), 1-8.","raw_type":"Conference or Workshop Item"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W206948248","https://openalex.org/W1606576718","https://openalex.org/W1953793983","https://openalex.org/W1967377294","https://openalex.org/W1970504153","https://openalex.org/W1975501109","https://openalex.org/W1976067945","https://openalex.org/W1980178290","https://openalex.org/W2003857027","https://openalex.org/W2004360097","https://openalex.org/W2010172797","https://openalex.org/W2014673260","https://openalex.org/W2016574277","https://openalex.org/W2016959837","https://openalex.org/W2049981393","https://openalex.org/W2050253520","https://openalex.org/W2085261163","https://openalex.org/W2108467139","https://openalex.org/W2109635530","https://openalex.org/W2111308925","https://openalex.org/W2118877769","https://openalex.org/W2129826981","https://openalex.org/W2137281791","https://openalex.org/W2138787968","https://openalex.org/W2142702104","https://openalex.org/W2144205281","https://openalex.org/W2145023731","https://openalex.org/W2211420311","https://openalex.org/W2512653618","https://openalex.org/W2529938554","https://openalex.org/W6676770471","https://openalex.org/W6677548441"],"related_works":["https://openalex.org/W2979950214","https://openalex.org/W87609089","https://openalex.org/W2414561716","https://openalex.org/W3024737167","https://openalex.org/W3161199934","https://openalex.org/W2303855011","https://openalex.org/W2312326526","https://openalex.org/W2412578866","https://openalex.org/W3105866016","https://openalex.org/W4312703710"],"abstract_inverted_index":{"New":[0],"vision":[1],"sensors,":[2],"such":[3],"as":[4],"the":[5,21,38,64,99,111,119,150,154,161,169],"Dynamic":[6],"and":[7,16,53,67,84,114,140],"Active-pixel":[8],"Vision":[9],"sensor":[10,19,65,101,155,171],"(DAVIS),":[11],"incorporate":[12],"a":[13,76,93],"conventional":[14,41],"camera":[15],"an":[17,129],"event-based":[18,46,103,130,165],"in":[20,110,156],"same":[22],"pixel":[23],"array.":[24],"These":[25],"sensors":[26],"have":[27],"great":[28],"potential":[29],"for":[30,98],"robotics":[31],"because":[32],"they":[33],"allow":[34],"us":[35],"to":[36,62,128],"combine":[37],"benefits":[39],"of":[40,45,75,78,121,153],"cameras":[42],"with":[43,69,168],"those":[44],"sensors:":[47],"low":[48],"latency,":[49],"high":[50,54],"temporal":[51],"resolution,":[52],"dynamic":[55],"range.":[56],"However,":[57],"new":[58],"algorithms":[59],"are":[60,107,125],"required":[61],"exploit":[63],"characteristics":[66],"cope":[68],"its":[70],"unconventional":[71],"output,":[72],"which":[73],"consists":[74],"stream":[77,120],"asynchronous":[79],"brightness":[80],"changes":[81],"(called":[82],"\u201cevents\u201d)":[83],"synchronous":[85],"grayscale":[86,112],"frames.":[87],"In":[88],"this":[89],"paper,":[90],"we":[91],"present":[92],"low-latency":[94],"visual":[95,131,166],"odometry":[96,132,167],"algorithm":[97,133],"DAVIS":[100,170],"using":[102,118,172],"feature":[104,173],"tracks.":[105,174],"Features":[106],"first":[108,162],"detected":[109],"frames":[113],"then":[115,126],"tracked":[116],"asynchronously":[117],"events.":[122],"The":[123],"features":[124],"fed":[127],"that":[134,145],"tightly":[135],"interleaves":[136],"robust":[137],"pose":[138],"optimization":[139],"probabilistic":[141],"mapping.":[142],"We":[143],"show":[144],"our":[146],"method":[147],"successfully":[148],"tracks":[149],"6-DOF":[151],"motion":[152],"natural":[157],"scenes.":[158],"This":[159],"is":[160],"work":[163],"on":[164]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":35},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":25},{"year":2019,"cited_by_count":24},{"year":2018,"cited_by_count":14},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":2}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}