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Simultaneous localization and mapping (SLAM) technology is widely used in smart factories, logistics, unmanned vehicles, high precision mapping and other fields, where inaccurate positioning or even failure is the key problem to be improved and solved by LiDAR SLAM algorithm. In this paper, we propose a loosely coupled SLAM algorithm based on lidar and inertial measurement unit (IMU). Firstly, linear interpolation of the laser point cloud is performed by introducing IMU data to correct the point cloud distortion. Second, a height threshold and a dynamic clustering threshold are introduced in the point cloud segmentation part to determine the classification. Again, the cumulative error is effectively avoided by using Scan-Context descriptors, and the next step is executed to find matching loopback frames based on the Euclidean distance, and the loopback constraints are added after the successful verification of both. Finally, a local map consisting of key frames is aligned with the current frame to obtain the inter-frame matching error, and then a joint optimization function is constructed jointly with the IMU pre-integration error and the loopback detection constraint to solve the laser odometer pose. The results on the KITTI dataset show that the improved laser odometer accuracy is higher than that of the LeGO-LOAM scheme in compliance with the odometer real-time requirements.
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