Mitigating Shadows in LIDAR Scan Matching Using Spherical Voxels
Mitigating Shadows in LIDAR Scan Matching Using Spherical Voxels
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DOI：
10.1109/lra.2022.3216987
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发表时间：
2022-08
影响因子：
5.2
通讯作者：
M. McDermott;J. Rife
中科院分区：
文献类型：
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作者：
M. McDermott;J. Rife
在这封信中，我们提出了一种方法，以减轻阴影误差的激光雷达扫描匹配，通过引入一个预处理步骤的基础上，球面网格。由于网格与LIDAR光束对齐，因此相对容易消除导致LIDAR扫描匹配中的系统误差的阴影边缘。正如我们通过对来自机械旋转多通道LIDAR单元的真实的和合成数据的测试所示，我们提出的算法提供了比地平面去除更好的结果，地平面去除是最常见的现有阴影缓解策略。与地平面移除不同，我们的方法适用于任意地形（例如城市墙壁上的阴影，丘陵地形中的阴影），同时保留地面上的关键激光雷达点，这些点对于估计高度，俯仰和滚动的变化至关重要。在我们的实验中，我们演示了我们的技术如何大幅减少真实的LIDAR点云数据上NDT扫描配准的误差（与标准的笛卡尔体素网格相比），然后在模拟环境中进行蒙特-卡罗试验，以演示我们提出的技术如何消除距离阴影引入的系统偏差。
In this letter we propose an approach to mitigate shadowing errors in LIDAR scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the LIDAR beam, it is relatively easy to eliminate shadow edges which cause systematic errors in LIDAR scan matching. As we show through testing on real and synthetic data from a mechanically spinning multi-channel LIDAR unit, our proposed algorithm provides better results than ground plane removal, the most common existing strategy for shadow mitigation. Unlike ground plane removal, our method applies to arbitrary terrains (e.g. shadows on urban walls, shadows in hilly terrain) while retaining key LIDAR points on the ground that are critical for estimating changes in height, pitch, and roll. In our experiments, we demonstrate how our technique drastically reduces error in NDT scan registration (compared to a standard Cartesian voxel grid) on real LIDAR point cloud data, and then conduct Monte-Carlo trials in a simulated environment to demonstrate how our proposed technique eliminates the systemic bias introduced by range-shadowing.