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ROVO:Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems

Title
ROVO:Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems
Author
임종우
Keywords
Cameras; Robot vision systems; Visual odometry; Calibration; Pose estimation
Issue Date
2019-05
Publisher
IEEE
Citation
IEEE International Conference on Robotics and Automation, Page. 6344-6350
Abstract
In this paper we propose a robust visual odometry system for a wide-baseline camera rig with wide field-of-view (FOV) fisheye lenses, which provides full omnidirectional stereo observations of the environment. For more robust and accurate ego-motion estimation we adds three components to the standard VO pipeline, 1) the hybrid projection model for improved feature matching, 2) multi-view P3P RANSAC algorithm for pose estimation, and 3) online update of rig extrinsic parameters. The hybrid projection model combines the perspective and cylindrical projection to maximize the overlap between views and minimize the image distortion that degrades feature matching performance. The multi-view P3P RANSAC algorithm extends the conventional P3P RANSAC to multi-view images so that all feature matches in all views are considered in the inlier counting for robust pose estimation. Finally the online extrinsic calibration is seamlessly integrated in the backend optimization framework so that the changes in camera poses due to shocks or vibrations can be corrected automatically. The proposed system is extensively evaluated with synthetic datasets with ground-truth and real sequences of highly dynamic environment, and its superior performance is demonstrated. © 2019 IEEE.
URI
https://ieeexplore.ieee.org/document/8793758http://repository.hanyang.ac.kr/handle/20.500.11754/114932
ISBN
978-1-5386-6027-0
ISSN
2577-087X
DOI
10.1109/ICRA.2019.8793758
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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