Visual odometry with effective feature sampling for untextured outdoor environment

Authors:
Yuya Tamura;Masataka Suzuki;Akira Ishii;Yoji Kuroda
Affiliations:
Meiji University, Department of Mechanical Engineering, Kawasaki, Kanagawa, Japan;Meiji University, Department of Mechanical Engineering, Kawasaki, Kanagawa, Japan;Meiji University, Department of Mechanical Engineering, Kawasaki, Kanagawa, Japan;Meiji University, Department of Mechanical Engineering, Kawasaki, Kanagawa, Japan
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

In this paper, we propose stereo vision based visual odometry with an effective feature sampling technique for untextured outdoor environment. In order to extract feature points in untextured condition, we divide an image into some sections and affect suitable processes for each section. This approach can also prevent concentration of feature points, and the influence with a moving object can be reduced. Robust motion estimation is attained using the framework of 3- point algorithm and RANdom SAmple Consensus (RANSAC). Moreover, the accumulation error is reduced by keyframe adjustment. We present and evaluate experimental results for our system in outdoor environment. Proposed visual odometry system can localize the robot's position within 4% error in untextured outdoor environment.