Mobile robot monocular vision navigation based on road region and boundary estimation

Mobile robot monocular vision navigation based on road region and boundary estimation

We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Chin-Kai Chang, Siagian, C., Itti, L.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Estimation
Image color analysis
Image segmentation
Navigation
Roads
Robots
Robustness
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1050
container_issue
container_start_page 1043
container_title
container_volume
creator Chin-Kai Chang
Siagian, C.
Itti, L.
description We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60m.
doi_str_mv 10.1109/IROS.2012.6385703
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6385703</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6385703</ieee_id><sourcerecordid>6385703</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-1644f030716fffd8d687cb66ef6a08a6131ce1c1f6a3c1bbd7e6e6f9a1f563403</originalsourceid><addsrcrecordid>eNo9kN1KAzEQheMfWGsfQLzJC2zN7OxOspdSrBYqBbXXJdkkJdJuJLst9O3davVqzpxvOAOHsTsQYwBRPczeFu_jXEA-JlSlFHjGRpVUUJBEkFjCORvkUGImFNEFu_kDEi__Qamu2ahtP4UQfSYhVAO2fI0mbBxP0cSOb2MT691GJ74PbYgNb_Q-rHV3lEa3zvJepKgtT259NHVjuYm7xup04K7twvbn-JZdeb1p3eg0h2w5ffqYvGTzxfNs8jjPAsiyy4CKwgsUEsh7b5UlJWtD5DxpoTQBQu2ghn7FGoyx0pEjX2nwJWEhcMjuf3ODc271lfr36bA6NYTfUR1WzA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Mobile robot monocular vision navigation based on road region and boundary estimation</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Chin-Kai Chang ; Siagian, C. ; Itti, L.</creator><creatorcontrib>Chin-Kai Chang ; Siagian, C. ; Itti, L.</creatorcontrib><description>We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60m.</description><identifier>ISSN: 2153-0858</identifier><identifier>ISBN: 1467317373</identifier><identifier>ISBN: 9781467317375</identifier><identifier>EISSN: 2153-0866</identifier><identifier>EISBN: 9781467317351</identifier><identifier>EISBN: 1467317365</identifier><identifier>EISBN: 9781467317368</identifier><identifier>EISBN: 1467317357</identifier><identifier>DOI: 10.1109/IROS.2012.6385703</identifier><language>eng</language><publisher>IEEE</publisher><subject>Estimation ; Image color analysis ; Image segmentation ; Navigation ; Roads ; Robots ; Robustness</subject><ispartof>2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, p.1043-1050</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6385703$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6385703$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chin-Kai Chang</creatorcontrib><creatorcontrib>Siagian, C.</creatorcontrib><creatorcontrib>Itti, L.</creatorcontrib><title>Mobile robot monocular vision navigation based on road region and boundary estimation</title><title>2012 IEEE/RSJ International Conference on Intelligent Robots and Systems</title><addtitle>IROS</addtitle><description>We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60m.</description><subject>Estimation</subject><subject>Image color analysis</subject><subject>Image segmentation</subject><subject>Navigation</subject><subject>Roads</subject><subject>Robots</subject><subject>Robustness</subject><issn>2153-0858</issn><issn>2153-0866</issn><isbn>1467317373</isbn><isbn>9781467317375</isbn><isbn>9781467317351</isbn><isbn>1467317365</isbn><isbn>9781467317368</isbn><isbn>1467317357</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kN1KAzEQheMfWGsfQLzJC2zN7OxOspdSrBYqBbXXJdkkJdJuJLst9O3davVqzpxvOAOHsTsQYwBRPczeFu_jXEA-JlSlFHjGRpVUUJBEkFjCORvkUGImFNEFu_kDEi__Qamu2ahtP4UQfSYhVAO2fI0mbBxP0cSOb2MT691GJ74PbYgNb_Q-rHV3lEa3zvJepKgtT259NHVjuYm7xup04K7twvbn-JZdeb1p3eg0h2w5ffqYvGTzxfNs8jjPAsiyy4CKwgsUEsh7b5UlJWtD5DxpoTQBQu2ghn7FGoyx0pEjX2nwJWEhcMjuf3ODc271lfr36bA6NYTfUR1WzA</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Chin-Kai Chang</creator><creator>Siagian, C.</creator><creator>Itti, L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201210</creationdate><title>Mobile robot monocular vision navigation based on road region and boundary estimation</title><author>Chin-Kai Chang ; Siagian, C. ; Itti, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-1644f030716fffd8d687cb66ef6a08a6131ce1c1f6a3c1bbd7e6e6f9a1f563403</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Estimation</topic><topic>Image color analysis</topic><topic>Image segmentation</topic><topic>Navigation</topic><topic>Roads</topic><topic>Robots</topic><topic>Robustness</topic><toplevel>online_resources</toplevel><creatorcontrib>Chin-Kai Chang</creatorcontrib><creatorcontrib>Siagian, C.</creatorcontrib><creatorcontrib>Itti, L.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chin-Kai Chang</au><au>Siagian, C.</au><au>Itti, L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Mobile robot monocular vision navigation based on road region and boundary estimation</atitle><btitle>2012 IEEE/RSJ International Conference on Intelligent Robots and Systems</btitle><stitle>IROS</stitle><date>2012-10</date><risdate>2012</risdate><spage>1043</spage><epage>1050</epage><pages>1043-1050</pages><issn>2153-0858</issn><eissn>2153-0866</eissn><isbn>1467317373</isbn><isbn>9781467317375</isbn><eisbn>9781467317351</eisbn><eisbn>1467317365</eisbn><eisbn>9781467317368</eisbn><eisbn>1467317357</eisbn><abstract>We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60m.</abstract><pub>IEEE</pub><doi>10.1109/IROS.2012.6385703</doi><tpages>8</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2153-0858
ispartof 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, p.1043-1050
issn 2153-0858
2153-0866
language eng
recordid cdi_ieee_primary_6385703
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Estimation
Image color analysis
Image segmentation
Navigation
Roads
Robots
Robustness
title Mobile robot monocular vision navigation based on road region and boundary estimation
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T09%3A28%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Mobile%20robot%20monocular%20vision%20navigation%20based%20on%20road%20region%20and%20boundary%20estimation&rft.btitle=2012%20IEEE/RSJ%20International%20Conference%20on%20Intelligent%20Robots%20and%20Systems&rft.au=Chin-Kai%20Chang&rft.date=2012-10&rft.spage=1043&rft.epage=1050&rft.pages=1043-1050&rft.issn=2153-0858&rft.eissn=2153-0866&rft.isbn=1467317373&rft.isbn_list=9781467317375&rft_id=info:doi/10.1109/IROS.2012.6385703&rft_dat=%3Cieee_6IE%3E6385703%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467317351&rft.eisbn_list=1467317365&rft.eisbn_list=9781467317368&rft.eisbn_list=1467317357&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6385703&rfr_iscdi=true