Research On Visual Positioning And Path Planning Algorithm For Unmanned Vehicle

With the continuous development of artificial intelligence and people’s yearning for a better life,unmanned vehicle technology has gradually become a hot research topic,among which the Simultaneous Localization and Mapping(SLAM)technology and path planning are important aspects of unmanned vehicle technology.This article focuses on the study of visual SLAM algorithm and path planning algorithm,with the main research contents as follows:A host system was designed in the open-source Robot Operating System(ROS),and a functional package was compiled based on experimental needs,connected to the motion control module via serial protocol.At the same time,hardware components such as controllers,perception modules,and motion modules were selected.By studying visual SLAM algorithm and laser SLAM,the vehicle’s pose estimation and map construction were achieved,and an improved solution was proposed for loop closure detection,which improves the matching rate.Experimental verification was performed using the Kitti dataset to demonstrate its effectiveness.An improved strategy is proposed for the A* algorithm in path planning.In response to issues such as long estimated path trajectory time and large memory usage,a self-adaptive step size strategy is proposed to improve the A* algorithm.By setting the priority order of the path search direction based on the position relationship between the starting point,ending point,and obstacles,the redundant planning calculation on unreasonable directions can be reduced.Meanwhile,the judgment condition for reaching the end point is modified,which enables the trajectory to jump during path planning.To address problems such as multiple trajectory turning points and paths being too close to obstacles in traditional A* algorithm planning,optimization is carried out for nodes near obstacles,and a uniform B-spline curve fitting is applied to smooth the planned path trajectory,thereby solving the problem of multiple path corners and frequent turns in A* algorithm that cannot satisfy physical constraints.Experimental results show that the improved algorithm significantly reduces path planning time and effectively optimizes the trajectory planning.By building an experimental platform,the improved algorithms were tested and verified.It was demonstrated that the improved visual SLAM algorithm outperformed traditional methods in loop closure detection speed,and the improved A* algorithm significantly improved path planning efficiency compared to traditional A* algorithm.