ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

In view of the problems of low accuracy and poor real-time in the research of visual simultaneous localization and mapping, a RGB-D vision SLAM algorithm for indoor mobile robots was proposed. Firstly, feature points of RGB image were extracted by using oriented fast and rotated brief (ORB) algorithm, and matching point pair set was obtained by the bidirectional K-nearest neighbor (KNN) feature matching method based on fast library for approximate nearest neighbors (FLANN). The improved random sampling consistency algorithm (RE-RANSAC) was used to eliminate false matching points and estimate the 6D motion transformation model between two adjacent images, as the initial transformation model of GICP algorithm. The generalized iterative closest point algorithm (GICP) was used to obtain the optimized motion transformation model, and then the pose diagram was obtained. In order to improve the positioning accuracy, a random closed-loop detection link was introduced to reduce the cumulative error in the robot positioning process, and the pose diagram was optimized by using the general graph optimization (G2O) method to obtain the global optimal pose diagram and camera motion trajectory, and the global color dense point cloud map was finally generated. For the tested FR1 data sets, the minimum positioning error of the algorithm was 0.011m, the average positioning error was 0.0245m, and the average processing time of each frame was 0.032s, which can meet the requirement of rapid positioning and mapping of mobile robots.