基于强度扫描上下文回环检测的激光雷达SLAM算法

Lidar simultaneous localization and mapping (SLAM) can realize real-time positioning of robots and build environmental maps in unknown environments, and has received extensive attention in recent years. Classic laser SLAM algorithms such as LOAM and Lego-LOAM only rely on the geometric information of the point cloud for pose estimation, ignoring the uniqueness of the intensity information and can also be used for effective position recognition. In addition, the iterative calculation methods in these classical algorithms are used to compensate point cloud distortion. Although the accuracy is guaranteed, the high consumption calculation is cost. Therefore, a lidar SLAM based on the intensity scan context loop closure detection is proposed. At the same time, the geometric and intensity information of the point cloud are utilized, and intensity scan context (ISC) is used as the global descriptor for loop closure detection to reduce the drift error. In addition, point cloud distortion compensation is implemented using a non-iterative two-step method to reduce computational cost. Experiments based on outdoor open data sets and indoor data collection show that the proposed laser SLAM algorithm can effectively suppress the odometry pose drift of odometer, improve the pose accuracy by about 50% on average compared with only using point cloud geometric information, which ensure the real-time performance of the algorithm when the loop closure detection module is added.