Realizing robust six degrees of freedom (6DOF) state estimation and high-performance simultaneous localization and mapping (SLAM) for perceptually degraded scenes (such as underground tunnels, corridors, and roadways) is a challenge in robotics. To solve these problems, we propose a SLAM algorithm based on tightly coupled LiDAR-IMU fusion, which consists of two parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly, on the front end, an iterative Kalman filter is established to construct a tightly coupled LiDAR-Inertial Odometry (LIO). The state propagation process for the a priori position and attitude of a robot, which uses predictions and observations, increases the accuracy of the attitude and enhances the system robustness. Second, on the back end, we deploy a keyframe selection strategy to meet the real-time requirements of large-scale scenes. Moreover, loop detection and ground constraints are added to the tightly coupled framework, thereby further improving the overall accuracy of the 6DOF state estimation. Finally, the performance of the algorithm is verified using a public dataset and the dataset we collected. The experimental results show that for perceptually degraded scenes, compared with existing LiDAR-SLAM algorithms, our proposed algorithm grants the robot higher accuracy, real-time performance and robustness, effectively reducing the cumulative error of the system and ensuring the global consistency of the constructed maps.

Point cloud registration is the basis of real-time environment perception for robots using 3D LiDAR and is also the key to robust simultaneous localization and mapping (SLAM) for robots. Because LiDAR point clouds are characterized by local sparseness and motion distortion, the point cloud features of coal mine roadway environments show a weak texture and degradation. Therefore, for these environments, the traditional point cloud registration method to register directly will lead to problems, such as a decline in registration accuracy, z-axis drift, and map ghosting. To solve the above problems, we propose a point cloud registration method based on IMU preintegration with the sensor characteristics of LiDAR and IMU. The system framework of this method mainly consists of four modules: IMU preintegration, point cloud preprocessing, point cloud frame matching and point cloud registration. First, IMU sensor data are introduced, and IMU linear interpolation is used to correct the motion distortion in LiDAR scanning, and the IMU preintegration error function is constructed. Second, the point cloud segmentation is performed using the ground segmentation method of RANSAC to provide additional ground constraints for the z-axis displacement and to remove the unstable flawed points from the point cloud. On this basis, the LiDAR point cloud registration error function is constructed by extracting the feature corner points and feature plane points. Finally, the Gaussian Newton solution is used to optimize the constraint relationship between the LiDAR odometry frames to minimize the error function, complete the LiDAR point cloud registration and better estimate the position and pose of the mobile robot. The experimental results show that compared with the traditional point cloud registration method, the proposed method has a higher point cloud registration accuracy, success rate and computational efficiency. The LiDAR odometry constructed using this method can better reflect the authenticity of the robot trajectory and has higher trajectory accuracy and smaller absolute position and pose error.