Direct Visual Odometry in Low Light Using Binary Descriptors
Feature descriptors are powerful tools for photometrically and geometrically invariant image matching. To date, however, their use has been tied to sparse interest point detection, which is susceptible to noise under adverse imaging conditions. In this letter, we propose to use binary feature descri...
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| Veröffentlicht in: | IEEE robotics and automation letters 2017-04, Vol.2 (2), p.444-451 |
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| container_title | IEEE robotics and automation letters |
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| creator | Alismail, Hatem Kaess, Michael Browning, Brett Lucey, Simon |
| description | Feature descriptors are powerful tools for photometrically and geometrically invariant image matching. To date, however, their use has been tied to sparse interest point detection, which is susceptible to noise under adverse imaging conditions. In this letter, we propose to use binary feature descriptors in a direct tracking framework without relying on sparse interest points. This novel combination of feature descriptors and direct tracking is shown to achieve robust and efficient visual odometry with applications to poorly lit subterranean environments. |
| doi_str_mv | 10.1109/LRA.2016.2635686 |
| format | Article |
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This novel combination of feature descriptors and direct tracking is shown to achieve robust and efficient visual odometry with applications to poorly lit subterranean environments.</description><subject>Cameras</subject><subject>Hamming distance</subject><subject>Lighting</subject><subject>Low light vision</subject><subject>mapping</subject><subject>Robot vision systems</subject><subject>robust visual odometry</subject><subject>Robustness</subject><subject>SLAM</subject><subject>visual tracking</subject><subject>Visualization</subject><issn>2377-3766</issn><issn>2377-3766</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkMFKAzEQhoMoWGrvgpe8wNYkY5INeKmtVWGhINZryGYnNdLulmRFfHu3tIinf-Cfbxg-Qq45m3LOzG31OpsKxtVUKJCqVGdkJEDrArRS5__mSzLJ-ZMxxqXQYOSI3C9iQt_T95i_3Jaumm6HffqhsaVV902ruPno6TrHdkMfYuuGZoHZp7jvu5SvyEVw24yTU47Jevn4Nn8uqtXTy3xWFR6E6Qv0ddkoJlE2PtSuBBRKGMkFlF4j1CGAvlPaoUTOQDSBGce8Bu6dFBw4jAk73vWpyzlhsPsUd8MzljN7EGAHAfYgwJ4EDMjNEYmI-LeutTJcS_gFoBVWLw</recordid><startdate>201704</startdate><enddate>201704</enddate><creator>Alismail, Hatem</creator><creator>Kaess, Michael</creator><creator>Browning, Brett</creator><creator>Lucey, Simon</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201704</creationdate><title>Direct Visual Odometry in Low Light Using Binary Descriptors</title><author>Alismail, Hatem ; Kaess, Michael ; Browning, Brett ; Lucey, Simon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c329t-ecb8d605e5dcfba83e262951238c7e3bff37467ae5e1032df09a0c731ca521313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Cameras</topic><topic>Hamming distance</topic><topic>Lighting</topic><topic>Low light vision</topic><topic>mapping</topic><topic>Robot vision systems</topic><topic>robust visual odometry</topic><topic>Robustness</topic><topic>SLAM</topic><topic>visual tracking</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alismail, Hatem</creatorcontrib><creatorcontrib>Kaess, Michael</creatorcontrib><creatorcontrib>Browning, Brett</creatorcontrib><creatorcontrib>Lucey, Simon</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE robotics and automation letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Alismail, Hatem</au><au>Kaess, Michael</au><au>Browning, Brett</au><au>Lucey, Simon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Direct Visual Odometry in Low Light Using Binary Descriptors</atitle><jtitle>IEEE robotics and automation letters</jtitle><stitle>LRA</stitle><date>2017-04</date><risdate>2017</risdate><volume>2</volume><issue>2</issue><spage>444</spage><epage>451</epage><pages>444-451</pages><issn>2377-3766</issn><eissn>2377-3766</eissn><coden>IRALC6</coden><abstract>Feature descriptors are powerful tools for photometrically and geometrically invariant image matching. 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| subjects | Cameras Hamming distance Lighting Low light vision mapping Robot vision systems robust visual odometry Robustness SLAM visual tracking Visualization |
| title | Direct Visual Odometry in Low Light Using Binary Descriptors |
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