国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In the scene with moving objects, the traditional stereo visual odometry has large positioning error and low system robustness. In order to solve this problem effectively, a stereo visual odometry algorithm based on moving object detection and feature point removal is proposed. The motion transformation matrix brought by the camera motion is calculated by the two-dimensional sparse optical flow method, and then the motion of the camera is compensated by the transformation matrix. Further, the forward-backward inter-frame difference algorithm is used for moving object detection, and the dynamic object and the static object in the scene are initially distinguished. Using the detected moving object’s area in the image as a mask, some feature points on the dynamic object are removed, and then the static point set is used to continue to optimize the transformation matrix, and the dynamic object influence is continuously reduced, and is applied to the stereo vision odometry system. After TUM"s RGBD dataset test evaluation, the proposed algorithm effectively improves the visual odometry positioning accuracy under dynamic scene.