Research on improving LIO-SAM based on Intensity Scan Context

Abstract

During indoor mobile mapping, localization plays an important role. It is difficult for IMU, odometer, Lidar or SLAM algorithm singly to meet the high efficiency, real-time, accurate and robust performance. So the fusion of the data measured by these different methods is a current research hotspot. However, most of the researches still focus on the loosely coupled fusion based on filtering methods, and the data cannot be fully utilized. In this paper, based on LOM-SAM framework, the Intensity Scan Context is introduced to extract keyframes and detect loop closure which will provide a closed-loop factor, together with IMU pre-integration factor and Lidar odometry factor to construct the factor graph. Then incremental smoothing optimization algorithm is used to get high precision trajectory, realize the tightly-coupled Lidar and IMU positioning. The results show that the number of keyframes is reduced, the elevation error is effectively decreased, and the real-time performance is improved without decreasing the accuracy of LIO-SAM.