A Sparse Visual Odometry Technique Based on Pose Adjustment With Keyframe Matching

In this article we propose a sparse visual odometry model for RGB-D images. The technique utilizes the minimization of the photometric errors obtained from the edge features for pose adjustment. Different from the conventional feature-based approaches, the features are extracted on the edge images. It makes the feature matching more robust and the computation more efficient. Moreover, we introduce a posterior probability based on the different degree of exposure for each edge point. The weight is then adjusted to improve the pose estimation by keyframe matching according to the probability. Since the sensor noise will affect the feature extraction results and cause the inaccurate estimation, the epipolar geometry and mixture distribution are used for the depth value updates. The experiments carried out using public datasets and our own image sequences have demonstrated the effectiveness of the proposed technique.