Conference item
DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
- Abstract:
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This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of them have demonstrated superior performance, they usually need to be carefully designed and specifically fine-tuned to work well in different environments. Some prior knowledge is also required to recover an absolute scale for monocular VO. This paper presen...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Accepted manuscript, pdf, 3.9MB)
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- Publisher copy:
- 10.1109/ICRA.2017.7989236
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Authors
Funding
Bibliographic Details
- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Journal:
- International Conference on Robotics and Automation Journal website
- Host title:
- ICRA 2017: IEEE International Conference on Robotics and Automation
- Publication date:
- 2017-07-24
- Acceptance date:
- 2017-01-15
- DOI:
- Source identifiers:
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695554
Item Description
- Pubs id:
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pubs:695554
- UUID:
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uuid:6e0ee820-29f3-42ab-bb44-c00363396c4d
- Local pid:
- pubs:695554
- Deposit date:
- 2017-05-17
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2017
- Notes:
- © 2017 IEEE. This article was presented at ICRA 2017: IEEE International Conference on Robotics and Automation (29 May - 3 June 2017: Singapore). This is the accepted manuscript version of the article. The final version is available online from IEEE at: [10.1109/ICRA.2017.7989236]
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