融合激光雷达与双层地图模型的智能车定位

Intelligent Vehicle Positioning by Fusing LiDAR and Double-layer Map Model

为提高智能车定位精度,提出了一种融合激光雷达与双层地图模型的智能车定位方法。该双层地图模型在车道图层基础上,增加基于激光点云的稀疏特征图层。稀疏特征地图由车辆位姿、2D强度特征和3D特征3部分组成,可为智能车定位提供精确的位置参考,以有效降低累积定位误差。此外,本文利用激光雷达强度信息提取车道线,为智能车定位提供高精度的、线性的横向位置约束。在定位过程中,引入Kalman滤波框架完成激光雷达与双层地图之间的有效融合。其中,状态预测过程利用车辆的运动约束构建短时间匀速运动模型,观测变量包括激光里程计定位结果、基于车道图层的横向位置约束和基于稀疏特征图层的定位结果。为了验证本文算法的有效性,在校园和城市道路环境下进行了测试。结果表明:本文提出的融合定位算法能在不同环境中将现有定位方法的定位误差降低40%~60%,定位相对误差小于0.3%。

In order to enhance the positioning accuracy of intelligent vehicles,a method fusing LiDAR and double-layer map model is proposed,in which the double-layer map model is created by adding laser pointcloud-based sparse feature map on the top of lane map,and the sparse feature map consists of the position and azimuth of vehicles,2D intensity features and 3D features.The sparse feature map can provide an accurate position reference for intelligent vehicle positioning,effectively reducing accumulative positioning error.In addition,the lane lines are extracted from the LiDAR intensity data to provide highly accurate and linear lateral position constraints.During positioning,a Kalman filter framework is introduced to fulfill the effective fusion of LiDAR and double-layer map,in which the process of state prediction utilizes the motion constraints of vehicle to construct the short-time and constant-speed movement model and to observe the variables including the results of laser odometer positioning,the lateral position constraints based on lane map layer and the positioning based on sparse feature map layer.Tests and measurements are conducted on both campus and urban road environment to verify the effectiveness of the proposed algorithm.The results show that the fusion positioning algorithm proposed can reduce the positioning error by 40%~60%under different environments,with a relative positioning error less than 0.3%.