Selective visual odometry for accurate AUV localization
In this paper we present a stereo visual odometry system developed for autonomous underwater vehicle localization tasks. The main idea is to make use of only highly reliable data in the estimation process, employing a robust keypoint tracking approach and an effective keyframe selection strategy, so...
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| Veröffentlicht in: | Autonomous robots 2017, Vol.41 (1), p.133-143 |
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| container_title | Autonomous robots |
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| creator | Bellavia, Fabio Fanfani, Marco Colombo, Carlo |
| description | In this paper we present a stereo visual odometry system developed for autonomous underwater vehicle localization tasks. The main idea is to make use of only highly reliable data in the estimation process, employing a robust keypoint tracking approach and an effective keyframe selection strategy, so that camera movements are estimated with high accuracy even for long paths. Furthermore, in order to limit the drift error, camera pose estimation is referred to the last keyframe, selected by analyzing the feature temporal flow. The proposed system was tested on the KITTI evaluation framework and on the New Tsukuba stereo dataset to assess its effectiveness on long tracks and different illumination conditions. Results of a live archaeological campaign in the Mediterranean Sea, on an AUV equipped with a stereo camera pair, show that our solution can effectively work in underwater environments. |
| doi_str_mv | 10.1007/s10514-015-9541-1 |
| format | Article |
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Results of a live archaeological campaign in the Mediterranean Sea, on an AUV equipped with a stereo camera pair, show that our solution can effectively work in underwater environments.</description><subject>Archaeology</subject><subject>Artificial Intelligence</subject><subject>Autonomous underwater vehicles</subject><subject>Computer Imaging</subject><subject>Control</subject><subject>Engineering</subject><subject>Localization</subject><subject>Mechatronics</subject><subject>Odometers</subject><subject>Pattern Recognition and Graphics</subject><subject>Robotics</subject><subject>Robotics and Automation</subject><subject>Tracks (paths)</subject><subject>Vision</subject><issn>0929-5593</issn><issn>1573-7527</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kEtLAzEURoMoWKs_wN2A6-i9eTSTZSm-QHBhdRvSPGTKtKnJTKH-eqeM4MrV3ZzzXTiEXCPcIoC6KwgSBQWUVEuBFE_IBKXiVEmmTskENNNUSs3PyUUpawDQCmBC1Ftog-uafaj2TeltWyWfNqHLhyqmXFnn-my7UM3fP6o2Ods237Zr0vaSnEXblnD1e6dk-XC_XDzRl9fH58X8hToudUdnuKpFtAy448F7VougAsTIoJ4Jr7jnvsbVKlqphdM-KvTeWimkiM7NgE_JzTi7y-mrD6Uz69Tn7fDRMCY1CI68HigcKZdTKTlEs8vNxuaDQTDHPGbMY4Y85pjH4OCw0SkDu_0M-W_5f-kHlQ9nrg</recordid><startdate>2017</startdate><enddate>2017</enddate><creator>Bellavia, Fabio</creator><creator>Fanfani, Marco</creator><creator>Colombo, Carlo</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>S0W</scope></search><sort><creationdate>2017</creationdate><title>Selective visual odometry for accurate AUV localization</title><author>Bellavia, Fabio ; 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| subjects | Archaeology Artificial Intelligence Autonomous underwater vehicles Computer Imaging Control Engineering Localization Mechatronics Odometers Pattern Recognition and Graphics Robotics Robotics and Automation Tracks (paths) Vision |
| title | Selective visual odometry for accurate AUV localization |
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