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31 January 2020 Monocular visual odometry based on hybrid parameterization
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114332A (2020)
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Visual odometry (VO) is one of the most challenging techniques in computer vision for autonomous vehicle/vessels. In VO, the camera pose that also represents the robot pose in ego-motion is estimated analyzing the features and pixels extracted from the camera images. Different VO techniques mainly provide different trade-offs among the resources that are being considered for odometry, such as camera resolution, computation/communication capacity, power/energy consumption, and accuracy. In this paper, a hybrid technique is proposed for camera pose estimation by combining odometry based on triangulation using the long-term period of direct-based odometry and the short-term period of inverse depth mapping. Experimental results based on the EuRoC data set shows that the proposed technique significantly outperforms the traditional direct-based pose estimation method for Micro Aerial Vehicle (MAV), keeping its potential negative effect on performance negligible.
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Sherif A. S. Mohamed, Mohammad-Hashem Haghbayan, Jukka Heikkonen, Hannu Tenhunen, and Juha Plosila "Monocular visual odometry based on hybrid parameterization", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114332A (31 January 2020);


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