基于IMU与激光雷达紧耦合的混合匹配算法

针对线、面特征匹配的激光雷达测距与地图构建算法(Lightweight and Ground-Optimized Lidar Odometry And Mapping,LeGO-LOAM)在自动导引运输车(Automated Guided Vehicle,AGV)室内室外实时建图与定位时,易出现激光里程计累积误差大和旋转估计不准确等问题,本工作采用惯性测量单元(Inertial Measurement Unit,IMU)与激光雷达紧耦合的LeGO-LOAM算法,通过IMU为激光雷达提供的初始位姿信息,构建IMU与激光雷达联合误差函数,实现位姿共同迭代优化.其中,对于室外结构化信息较少时,在点对点的迭代最近点算法(Iterative Closest Point,ICP)较高定位精度的基础上,结合LeGO-LOAM算法和ICP算法互补性,进一步提出基于IMU与激光雷达紧耦合的混合匹配算法:当环境中结构信息较多时,激光里程计采用LeGO-LOAM算法,而当环境中结构化信息较少时采用ICP算法.实验结果表明,基于IMU与激光雷达紧耦合的混合匹配算法可有效降低激光里程计相对位姿误差和累积误差,提高AGV小车定位精度以消除部分地图重影.

Hybrid Scanning Matching Algorithm Based on Tight Coupling of IMU and Lidar

Large cumulative errors of laser odometers and inaccurate rotation estimation can be encountered when the Lightweight and Ground-Optimized Lidar Odometry And Mapping (LeGO-LOAM) with line and surface feature matching is used for real-time mapping and positioning of an automated guided vehicle indoors and outdoors. In view of these problems, this work adopts the LeGO-LOAM with tightly coupled Inertial Measurement Unit (IMU) and lidar to construct the joint error function of IMU and lidar with the initial position and pose information provided by IMU for the lidar. As a result, the joint iterative optimization of position and pose is achieved. To cope with the outdoor cases with less structured information, a hybrid matching algorithm depending on the tight coupling of IMU and lidar is further proposed on the basis of the high positioning accuracy of the point-to-point Iterative Closest Point (ICP) algorithm in light of the complementarity between LeGO-LOAM and ICP algorithms. When there is much structured information in the environment, the laser odometer employs the LeGO-LOAM algorithm, and ICP algorithm functions in the case of less structured information. The experimental results show that the hybrid matching algorithm based on the tight coupling of IMU and lidar can effectively reduce the relative pose error and cumulative error of the laser odometer. In addition, it is able to eliminate some map ghosting by improving the positioning accuracy of Automated Guided Vehicles (AGVs).