School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China.
Sensors (Basel). 2023 Jan 11;23(2):843. doi: 10.3390/s23020843.
LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.
激光雷达的布局和视野选择在基于高精度地图自动导航的重定位地图中检测车辆的相对位置和姿态中起着重要作用。当激光雷达视野被遮挡或激光雷达位置错位时，这很容易导致重定位丢失或重定位精度降低。本文提出了一种基于高精度地图正态分布变换（NDT）重定位的激光雷达布局和视野选择方法，以解决当遮挡视野过大时，NDT 重定位误差大且位置丢失的问题。为了模拟真实的放置环境和被障碍物遮挡的激光雷达，使用 ROI 算法对激光雷达点云进行裁剪，得到不同大小的激光雷达点云数据。首先对裁剪后的点云数据进行下采样，然后进行重定位。记录 NDT 重定位的下采样点作为有效匹配点。使用均方根误差（RMSE）值和有效匹配点优化激光雷达点云数据的方向和角度设置。结果表明，在具有复杂道路条件的城市场景中，单位角度内前后匹配点比左右匹配点多。NDT 重定位算法的匹配点越多，重定位精度越高。增加前后激光雷达视野可以防止重定位丢失。增加左右激光雷达视野可以提高重定位精度。
