基于固态激光雷达融合2D激光雷达的建图研究

Mapping Research Based on Solid-State LiDAR Fusion with 2D LiDAR

针对传统2D激光雷达建图存在空间环境信息获取不完全的问题,提出一种基于Gmapping算法融合固态激光雷达和2D激光雷达的建图策略。首先,对固态激光雷达点云数据进行平面投影,利用生成的激光数据结合Gmapping算法中最优粒子轨迹建立栅格地图后,再与最优粒子携带的栅格地图融合生成的融合地图,实现对空间障碍物的识别。其次,为提升建图精度,使用扩展卡尔曼滤波(EKF)对轮式里程计(WO)、激光里程计(LO)和惯性测量单元(IMU)进行动态权重融合,解决因车轮打滑或激光里程计在低特征环境下特征匹配失败等因素造成的融合里程计精度下降问题。最后,对融合地图和融合里程计算法进行测试实验。实验结果表明,融合地图可以正确识别空间障碍物,融合里程计在平均定位精度上相较于传统方法提升17.0%。

To address the issue of incomplete spatial environment information acquisition in traditional twodimensional(2D)light detection and ranging(LiDAR)mapping,we propose a mapping strategy that leverages the fusion of solidstate LiDAR and 2D LiDAR using the Gmapping algorithm.First,we initiate a planar projection on the solidstate LiDAR point cloud data.Subsequently,the resultant laser data are combined with the optimal particle trajectory within the Gmapping algorithm to construct a grid map.This grid map is then integrated with the grid map carried by the optimal particle,resulting in a fused map designed to identify spatial obstacles.To enhance mapping accuracy,we employe an extended Kalman filter for the dynamic fusion of weights associated with the wheel odometer,laser odometer,and inertial measurement unit.This approach addresses the challenges posed by reduced fusion odometer accuracy in scenarios involving factors such as slippage or featurematching failures of the laser odometer in environments with limited features.Subsequently,we conducte testing experiments on the fused map and the fusion mileage calculation method.The experimental outcomes demonstrate that the fused map effectively identifies spatial obstacles and the fused odometer exhibits an average positioning accuracy improvement of 17.0%compared to traditional methods.