In this paper, we propose a method to improve ego-motion estimation. In the proposed method, feature points from input binocular stereo image pairs are detected using the Speeded Up Robust Features method and descriptor vectors are generated for the features. Semi-Global Matching is then performed to ensure the quality and quantity of the matching features, which are used in conjunctions with camera parameters to calculate the 3D coordinates for the image features. Kalman filter and adaptive matching are the used to perform feature tracking. The calculated 3D coordinates, as well as the 2D projections and camera parameters are used in Efficient Perspective-n-Point Camera Pose Estimation to find the camera motions. Finally, Sparse Bundle Adjustment is used to perform optimization of the estimated motions.
['Ting-Hsiang Huang', 'Chen-Chi Chuang', 'Yu-Hsiang Chang', 'Chia-Yen Chen']
Adaptive Feature Tracking with Kalman Filter for Ego-Motion Estimation