Optimal Target Shape for LiDAR Pose Estimation
Optimal Target Shape for LiDAR Pose Estimation
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DOI：
10.1109/lra.2021.3138779
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发表时间：
2022-04-01
影响因子：
5.2
通讯作者：
Grizzle, Jessy W.
中科院分区：
文献类型：
--
作者：
Huang, Jiunn-Kai;Clark, William;Grizzle, Jessy W.
目标是必不可少的问题，如在混乱或无纹理环境中的目标跟踪，相机（和多传感器）校准任务，同时定位和映射（SLAM）。这些任务的目标形状通常是对称的（正方形、矩形或圆形），并且对于结构化的、密集的传感器数据（例如像素阵列（即，图像）。然而，当使用稀疏传感器数据（诸如LiDAR点云）时，对称形状导致姿态模糊，并且遭受LiDAR的量化不确定性。这封信介绍了优化目标形状的概念，以消除激光雷达点云的姿态模糊。目标被设计成在相对于LiDAR的旋转和平移下在边缘点处引起大梯度，以改善与点云稀疏性相关联的量化不确定性。此外，给定目标形状，我们提出了一种方法，利用目标的几何形状来估计目标的顶点，同时全局估计的姿态。仿真和实验结果（由运动捕捉系统验证）证实，通过使用最佳形状和全局求解器，我们实现厘米的平移误差和旋转几度，即使当一个部分照明的目标被放置在30米远。所有的实现和数据集都可以在https://github.com/UMich-BipedLab/global_pase_estimation_for_optimal_shape上找到。
Targets are essential in problems such as object tracking in cluttered or textureless environments, camera (and multisensor) calibration tasks, and simultaneous localization and mapping (SLAM). Target shapes for these tasks typically are symmetric (square, rectangular, or circular) and work well for structured, dense sensor data such as pixel arrays (i.e., image). However, symmetric shapes lead to pose ambiguity when using sparse sensor data such as LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR. This letter introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. A target is designed to induce large gradients at edge points under rotation and translation relative to the LiDAR to ameliorate the quantization uncertainty associated with point cloud sparseness. Moreover, given a target shape, we present a means that leverages the target's geometry to estimate the target's vertices while globally estimating the pose. Both the simulation and the experimental results (verified by a motion capture system) confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed 30 meters away. All the implementations and datasets are available at https://github.com/UMich-BipedLab/global_pase_estimation_for_optimal_shape.