Robust Structure and Motion Estimation by Auto-Scale Random Sample Consensus
This paper proposes a robust strategy for structure from planar motion estimation. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. So the algorithm can deal with a great number of outliers induced by an automatic matching computation. With the adoption of the auto-scale technique, the algorithm can be used in an entirely automatic application without any prior information or user set parameters. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. The experiments indoor and outdoor have been done to verify the feasibility of the algorithm. In the experiments, the feature correspondences in the image sequences are extracted and refined automatically by the relation of the stereo cameras and the property of the planar motion. The results show the algorithm is robust and efficient for applications in planar motions.
auto-scale structure from motion random sample consensus planar motion.
Chen Tai Yun-hui Liu
Department of Automation and Computer-Aided Engineering The Chinese University of Hong Kong Hong Kong
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
37-42
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）