Research On Mobile Robot Localization Technology Based On Solid-state Lidar SLAM

In SLAM(Simultaneous Localization And Mapping)technology,Li DAR is widely used for environmental perception,and recently,solid-state Li DAR has attracted much attention.Compared with mechanical Li DAR,solid-state Li DAR has the advantages of being lightweight,reliable,and low-cost,making it suitable for small devices such as drones and mobile robots.Moreover,in the commercially available Livox Horizon solid-state radar,the manufacturer has installed an IMU(Inertial Measurement Unit)to meet the needs of multi-sensor fusion positioning,making it more competitive in commercial applications.To address the issue of inaccurate positioning of mechanical Li DAR in outdoor open scenarios,this paper proposes a SLAM algorithm based on solid-state Li DAR and its built-in IMU fusion positioning.The main research contents of this paper are as follows:(1)To address the issue of inaccurate sensor observation data in scenarios where mobile robots move at high speeds or rotate,this paper designs an intrinsic calibration algorithm for the built-in 6-axis IMU of solid-state Li DAR.Based on the traditional multi-pose static calibration algorithm,a data acquisition strategy utilizing high-speed rotation of mobile robots is added,and the nonlinear parameters of the scale factor in the IMU are incorporated into the error calibration model of accelerometers and gyroscopes.The LM(Levenberg-Marquardt)algorithm is used to solve the intrinsic parameters,and the calibration algorithm is validated experimentally.(2)To address the problem of low efficiency in data association and matching in point cloud registration and mapping,this paper proposes a new incremental voxel space data structure for storing local point clouds.Voxel space is divided using pseudo-Hilbert curves,and a spatial hash function is used to manage point cloud information corresponding to voxels,improving the real-time performance and accuracy of point cloud registration.(3)In the localization phase,the IEKF(Iterated Extended Kalman Filter)is used to update and predict system states such as velocity,position,attitude,acceleration,and gravity vector of the IMU and solid-state Li DAR.Based on this,the ESKF(Error-State Kalman Filter)is added to estimate the observation error states of the IMU and Li DAR,serving as a pre-step for the IEKF algorithm,ultimately improving the positioning accuracy of the entire Lidar Inertial Odometry(LIO)system.Finally,multiple sets of data are collected and experimented within the campus.The results show that the LIO-ESKF algorithm based on solid-state Li DAR outperforms traditional laser-inertial SLAM algorithms in open outdoor scenarios.