Research On Location And Path Planning Of Omnidirectional Vehicle Based On Visual Sensing

With the intelligence of industrial production and the flexibility of production lines,the working environment of omnidirectional automatic navigation vehicles is becoming increasingly complex,and the unstructured environment also makes traditional navigation methods no longer applicable.This article analyzes the two major categories of SLAM technologies,laser and vision,and addresses the issue of insufficient accuracy and system robustness in the construction of single sensor SLAM algorithms.By improving the front-end and back-end of the SLAM algorithm that combines laser and vision,the improved algorithm has strong applicability,high reliability,and good robustness.The use of improved RRT algorithm on established maps provides a path planning method for omnidirectional vehicles.The specific research content is as follows:The motion model of the omnidirectional vehicle was derived,and the vehicle body was designed according to different requirements of actual tasks.The sensors used in the omnidirectional vehicle were selected,and the observation models of cameras and radar were established.A calibration experiment was conducted based on a depth camera,and the relative relationship between the camera and the Li DAR was derived.Three feature point methods for indirect visual odometry were screened and validated,followed by the ORB feature point method;In the matching scheme,the FLANN algorithm is improved by filtering and sorting ideas,which improves the accuracy of matching and reduces matching time;After evaluating the noise problem in the laser point cloud of the laser odometer through the evaluation function,the algorithm for interpolating missing points using weighted least squares fitting was improved to ensure the effectiveness of point cloud noise reduction.Improve the front-end and back-end of the ORB-SLAM2 framework.At the front end,the feature point cloud processed by the camera is backprojected,and a filtering function is used to filter point clouds with different heights.The laser point cloud and the processed camera feature point cloud are fitted using weighted least squares and linear methods,respectively,to improve the accuracy of the improved algorithm.In the backend,the improved feature point matching algorithm is integrated into the framework and the estimation accuracy of the hybrid SLAM algorithm is verified using a dataset,obtaining the superiority of its estimation accuracy.In global path planning,the greedy RRT algorithm was improved by using the offset target algorithm when expanding random points.After experimental verification,it was found that this algorithm outperformed the original algorithm in terms of time and path length.A simulation environment was built using the system to simulate the SLAM algorithm.An experimental platform was built using the ROS system,selected sensors,and chassis systems.Indoor site simulation conditions were selected for actual positioning,mapping,and global path planning experiments,verifying the accuracy of the improved SLAM algorithm in mapping and the effectiveness of the path planning algorithm.