Visual Odometry with Improved Adaptive Feature Tracking
Chia‐Yen Chen, Jiahong Zhang, Bao Rong Chang · 2014
In visual odometry applications, tracking of features in a video sequence greatly impacts the accuracy of ego-motion estimation. A robust visual tracking has to take into account either geometric or photometric conditions to exclude outliers in the stage of motion estimation. In this work we propose a novel method to adaptively constrain the matching of features in time sequence. The control of matching is based on two major Euclidean properties, namely scale and angle. The scale constraint refers to the disparity image, and the angular criterion is derived from epipolar geometry. The proposed method is tested with two selected sequences from KITTI visual odometry benchmark datasets. Experimental results indicate promising improvement is attainable over a conventional feature tracker for visual odometry.