一种利用图像序列从平面运动中提取结构的鲁棒技术

Chen Tai, Yun-hui Liu
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引用次数: 3

摘要

本文提出了一种从平面运动的立体摄像机拍摄的两幅图像序列中恢复运动和结构的鲁棒方法。根据立体摄像机与运动特性的关系,自动提取和细化图像之间的特征对应关系。为了提高鲁棒性,在运动和结构估计中采用随机样本一致性(RANSAC)算法。与其他研究人员对极线几何形状的恢复不同,这里我们使用它来恢复二维运动,并且不仅排除了极线外的异常值,而且排除了极线内的异常值。同时利用RANSAC的思想进行结构估计,排除序列视图中的异常值。这项工作最重要的贡献是使该估计方案更加鲁棒和高效,以便在实际应用中使用。通过室内和室外实验验证了该算法的可行性。实验结果表明,该算法具有较好的鲁棒性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A robust technique for structure from planar motion using image sequences
This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondence between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, the random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other researchers to recover of epipolar geometry, here we use it to recover the 2D motion and to exclude the outliers not only out of the epipolar lines but also in them. Also the spirit of RANSAC is used in structure estimation to exclude the outlier from the sequence view. The most important contribution of this work is a way to make this estimation scheme more robust and efficient so as to be used in real applications. The experiments indoor and outdoor have been done to verify the feasibility of the algorithm. The results show the algorithm is robust and efficient for applications in planar motion.
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