Tracking keypoints through a video sequence is a crucial first step in the processing chain of many visual SLAM approaches. This paper presents a robust initialization method to provide the initial match for a keypoint tracker, from the 1st frame where a keypoint is detected to the 2nd frame, that is: when no depth information is available. We deal explicitly with the case of long displacements. The starting position is obtained through an optimization that employs a distribution of motion priors based on pyramidal phase correlation, and epipolar geometry constraints. Experiments on the KITTI dataset demonstrate the significant impact of applying a motion prior to the matching. We provide detailed comparisons to the state-of-the-art methods.
CITATION STYLE
Fanani, N., Barnada, M., & Mester, R. (2015). Motion priors estimation for robust matching initialization in automotive applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9474, pp. 115–126). Springer Verlag. https://doi.org/10.1007/978-3-319-27857-5_11
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