An Improved Visual Odometry Optimization Algorithm Based on Kinect Camera

A visual odometry optimization algorithm based on Kinect camera is proposed. The optimization algorithm uses Oriented FAST and Rotated BRIEF (ORB) algorithm to extract and matching feature point pairs, culls the mismatching feature point pairs through the bidirectional consistency check, the constraints based on the color space information and the Random Sample Consensus (RANSAC) algorithm, and then indexes the corresponding three-dimensional (3D) information of the eventually matching feature point pairs. It uses the least squares method to calculate the rotation and translation matrix so as to complete motion estimation between the two images, and then implements the local optimization of the motion estimation using the Sparse Bundle Adjustment (SBA) algorithm, eventually it could draw a global visual odometry simulated trajectory according to the optimized motion estimation. The experiment result shows that this method has high accuracy and robustness.