Research on Complex Indoor Environment Mapping Based on LiDAR and RGB-D Camera Fusion

With the continuous progress of science and technology and the improvement of social productivity, mobile robots have been widely used in military, medical, logistics and other fields. High-precision mapping technology for complex indoor environments has become one of the focuses of scholars' attention and research hotspots. Traditional single sensors (e.g., LiDAR or cameras) have limitations in dynamic, low-texture or light-varying environments. In this paper, we propose a mapping method based on the multi-sensor fusion of LIDAR (RPLIDAR A2M8-R3) with RGB-D camera and odometry to improve the accuracy and robustness of mapping in complex indoor environments by fusing the accurate distance measurement of LIDAR with the rich visual information of camera. Firstly, a ROS-based robotic hardware and software platform is constructed, integrating LiDAR (RPLIDAR A2M8-R3) and RGB-D camera. Secondly, sensor data preprocessing such as LiDAR is optimised by various algorithms. Finally, the iRTAB-Map fusion modelling strategy is used to achieve efficient fusion of multi-source data for map building. Experiments show that the fusion system is better than the single-sensor scheme in terms of 2D raster map accuracy and 3D point cloud completeness, and the navigation accuracy is not only high but also the real-time observation of obstacles and road conditions through the camera, which provides reliable technical support for indoor complex scene mapping.