Research on Laser SLAM Mapping and Navigation Algorithm Based on Improved Cartographer Algorithm and Path Planning Algorithm

Aiming at the problems of high environmental sensitivity, poor mapping effect and poor autonomous navigation and positioning accuracy of traditional laser SLAM mapping in complex environments, a lidar mapping algorithm based on the improved Cartographer algorithm and a path planning algorithm based on multi-algorithm fusion are proposed. Firstly, the radar data is dedistorted by using the feature extraction method of the curvature size in LIO-SAM to generate high-quality point clouds. Then, the adaptive untraced Kalman filtering algorithm is used to fuse the IMU and odometer data to obtain more accurate pose estimation. When establishing the path planning algorithm, the RRT-Connect algorithm is used as the global path planning, and the artificial potential field method is used as the local path planning. The RRT-Connect algorithm is utilized to construct an optimal path from the initial point to the target point, and the artificial potential field method is used for real-time obstacle avoidance and local path correction during the operation process. By integrating the two algorithms, autonomous navigation operations in complex environments are achieved. Finally, the mapping algorithm and navigation algorithm were verified in the gazebo environment and the outdoor real environment. Experiments show that the mapping accuracy of the optimized Cartographer algorithm is higher. Meanwhile, when using the path planning algorithm to achieve autonomous navigation, the positioning of navigation is more accurate when the speed is less than 0.3m/s, and it is suitable for the navigation requirements of complex outdoor environments.