A LiDAR and camera fusion-based approach to mapping and navigation

For the map building problem of laser and vision fusion, a novel fusion algorithm is proposed in this paper. At first, the algorithm preprocesses the original point cloud obtained by the depth camera with voxel filtering, coordinate transformation, direct-pass filtering, dimensionality reduction processing and conversion of the polar coordinates. Finally, two types of data fusion are performed. The paper designs three experiments on data fusion, map building, and robotics navigation. The experimental results show that the fused data of camera and LiDAR can reflect more useful information from the surrounding environment compared with the 2D LiDAR data, and the environmental map built by the robot with the fused data for SLAM presents more comprehensive environmental information. Similarly, when the robot navigates with the fused data, the actual effect of obstacle avoidance and navigation is satisfactory.