旋转捷联惯导系统辅助的多线激光雷达新型SLAM方法

New SLAM Method of Multi-layer Lidar Assisted by Rotational Strapdown Inertial Navigation System

针对惯性传感器精度低下影响基于激光雷达/惯性信息融合的同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术性能的问题,提出了一种旋转捷联惯导系统辅助下的多线激光雷达SLAM优化方案。该方案探讨了基于模糊自适应卡尔曼滤波的旋转捷联惯导对准方法,在载体运动过程中完成载体姿态与惯性传感器误差的实时修正;在此基础上,将修正后的惯性传感器数据与激光雷达点云数据进行紧耦合模式下的信息融合,以提高载体在复杂场景中运动时定位与建图的精度和实时性。实验结果表明,基于旋转惯导与多线激光雷达信息融合的SLAM方案,在保证运算实时性的同时,有效提高了激光雷达/惯性里程计的定位性能,以及点云地图的准确性。

Focusing on the influence of low-accuracy inertial sensor on the performance of lidar/inertial SLAM,an optimized SLAM method by fusing information of multi-layer lidar and rotational strapdown inertial navigation system is studied.In this scheme,the rotating strapdown inertial navigation alignment method based on fuzzy adaptive Kalman filter is discussed,and the real-time correction of carrier attitude and inertial sensor error is completed in the process of carrier motion.Further more,the corrected inertial sensor data and LIDAR point cloud data are fused in tight coupling mode to improve the accuracy and real-time of positioning and mapping when the carrier moves in complex scenes.Experimental results show that the slam scheme based on rotating inertial navigation and multi-layer lidar information fusion not only ensures the real-time operation,but also effectively improves the positioning performance of lidar/inertial odometry and the accuracy of point cloud map.