Distribution-to-Distribution (D2D) point cloud registration algorithms are fast, interpretable, and perform well in unstructured environments. Unfortunately, existing strategies for predicting solution error for these methods are overly optimistic, particularly in regions containing large or extended physical objects. In this paper we introduce the Iterative Closest Ellipsoidal Transform (ICET), a novel 3D LIDAR scan-matching algorithm that re-envisions NDT in order to provide robust accuracy prediction from first principles. Like NDT, ICET subdivides a LIDAR scan into voxels in order to analyze complex scenes by considering many smaller local point distributions, however, ICET assesses the voxel distribution to distinguish random noise from deterministic structure. ICET then uses a weighted least-squares formulation to incorporate this noise/structure distinction into computing a localization solution and predicting the solution-error covariance. In order to demonstrate the reasonableness of our accuracy predictions, we verify 3D ICET in three LIDAR tests involving real-world automotive data, high-fidelity simulated trajectories, and simulated corner-case scenes. For each test, ICET consistently performs scan matching with sub-centimeter accuracy. This level of accuracy, combined with the fact that the algorithm is fully interpretable, make it well suited for safety-critical transportation applications. Code is available at https://github.com/mcdermatt/ICET

翻译：分布到分布（D2D）点云配准算法具有快速、可解释性强且在非结构化环境中表现良好的特点。然而，现有预测这些方法解误差的策略过于乐观，尤其在包含大型或长形物理对象的区域中。本文提出迭代最近椭球变换（ICET），一种新颖的3D激光雷达扫描匹配算法，旨在从根本上重新构建正态分布变换（NDT）以提供鲁棒的精度预测。与NDT类似，ICET将激光雷达扫描划分为体素，通过分析多个较小的局部点分布来处理复杂场景；但ICET评估体素分布以区分随机噪声与确定性结构。随后，ICET采用加权最小二乘公式，将这种噪声与结构的区分融入定位解的计算及解误差协方差的预测中。为验证我们精度预测的合理性，我们在三项激光雷达测试中验证了3D ICET，包括真实汽车数据、高保真模拟轨迹以及模拟极端场景。每项测试中，ICET均以亚厘米级精度持续完成扫描匹配。这一精度水平结合算法完全可解释的特性，使其非常适合安全关键型交通应用。代码开源地址：https://github.com/mcdermatt/ICET