Robust and Autonomous Stereo Visual-Inertial Navigation for Non-Holonomic Mobile Robots
- Authors
- Chae, Hee-Won; Choi, Ji-Hoon; Song, Jae-Bok
- Issue Date
- 9월-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Mobile robots; Cameras; Navigation; Wheels; Feature extraction; Robot vision systems; Autonomous navigation; visual-inertial systems; keyframes; wheeled mobile robots
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.69, no.9, pp.9613 - 9623
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 69
- Number
- 9
- Start Page
- 9613
- End Page
- 9623
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/53281
- DOI
- 10.1109/TVT.2020.3004163
- ISSN
- 0018-9545
- Abstract
- Unlike micro aerial vehicles, most mobile robots have non-holonomic constraints, which makes lateral movement impossible. Consequently, the vision-based navigation systems that perform accurate visual feature initialization by moving the camera to the side to ensure a sufficient parallax of the image are degraded when applied to mobile robots. Generally, to overcome this difficulty, a motion model based on wheel encoders mounted on a mobile robot is used to predict the pose of a robot, but it is difficult to cope with errors caused by wheel slip or inaccurate wheel calibration. In this study, we propose a robust autonomous navigation system that uses only a stereo inertial sensor and does not rely on wheel-based dead reckoning. The observation model of the line feature modified with vanishing-points is applied to the visual-inertial odometry along with the point features so that a mobile robot can perform robust pose estimation during autonomous navigation. The proposed algorithm, i.e., keyframe-based autonomous visual-inertial navigation (KAVIN) supports the entire navigation system and can run onboard without an additional graphics processing unit. A series of experiments in a real environment indicated that the KAVIN system provides robust pose estimation without wheel encoders and prevents the accumulation of drift error during autonomous driving.
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Collections - College of Engineering > Department of Mechanical Engineering > 1. Journal Articles
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