Obstacle avoidance algorithm based on sub-image segmentation and mapping point cloud space

The inspection of the working status of coal conveyor belts is an important part of ensuring safe production. As a conventional means of intelligent inspection, inspection robots are of great significance for ensuring the safe production of coal mines and realizing the reduction of personnel and the enhancement of safety underground. In order to improve the obstacle avoidance ability of mine inspection robots under long-distance and complex working conditions, adjust the travel route in real time and achieve the avoidance of obstacles, based on the inspection and obstacle avoidance system with the fusion of multi-source sensing of infrared cameras and lidars, the main work is as follows: A robot obstacle avoidance algorithm based on sub-image segmentation and mapping of point cloud space is proposed. Firstly, by taking infrared data as the boundary condition, the infrared image is divided into blocks to form sub-image units, and the point cloud space range is mapped with sub-images of different scales, thereby realizing the extraction of obstacle point clouds; and by using the projection method of each sub-image unit, the limitation of the three-dimensional point cloud in the target area is completed; then, by using the way of boundary constraints to reduce the total amount of point cloud data processing, the convergence speed of the algorithm and the extraction speed of the obstacle feature point clouds are improved. Finally, the simplification effect of the algorithm on the total amount of point clouds is verified through simulation analysis, the inversion accuracy of the maximum outer diameter of obstacles under different sub-image scales is simulated, and the real-time obstacle avoidance ability effect of the system applying this algorithm is verified. The experimental results show that when the side length of the sub-image is 10.0 mm, the maximum relative error is less than 1.53%, the convergence time of the algorithm is 1.243 s, and both the inversion accuracy of the obstacle outer diameter and the convergence speed meet the actual application requirements; the algorithm has a high accuracy rate and obstacle avoidance efficiency in static obstacle, dynamic obstacle and multi-robot obstacle avoidance environments, meets the needs of inspection robots for real-time environmental data collection and obstacle avoidance, and has a high practical application value.