An Adaptive Strategy For Monocular Visual Odometry
Pages 46 - 50
Abstract
Monocular Visual odometry is an important technique in mobile robot localization and navigation. This paper first empirically studies two kinds of commonly used monocular visual odometry (MVO): descriptor-based methods and optical flow based methods. Six representative scenes are extracted from KITTI and Karlsruhe datasets. Ten MVO algorithms are evaluated in terms of real-time performance and trajectory accuracy. Experimental results show that different MVO algorithms show different performance in different scenarios. Furthermore, an adaptive visual odometry(AVO) strategy is proposed on the basis of the experiment results. The changing environment is detected and the most suitable MVO algorithm is chosen dynamically according to a cost function. The experimental results show that the AVO method can obtain higher trajectory accuracy and better real-time performance.
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Index Terms
- An Adaptive Strategy For Monocular Visual Odometry
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Published In
December 2017
127 pages
ISBN:9781450353588
DOI:10.1145/3175603
Copyright © 2017 ACM.
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- Nanyang Technological University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 December 2017
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ICRAI 2017
ICRAI 2017: 2017 International Conference on Robotics and Artificial Intelligence
December 29 - 31, 2017
Shanghai, China
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