The typical point cloud sampling methods used in state estimation for mobile robots preserve a high level of point redundancy. The point redundancy slows down the estimation pipeline and can make real-time estimation drift in geometrically symmetrical and structureless environments. We propose a novel point cloud sampling method that is capable of lowering the effects of geometrical degeneracies by minimizing redundancy within the cloud. The proposed method is an alternative to the commonly used sparsification methods that normalize the density of points to comply with the constraints on the real-time capabilities of a robot. In contrast to density normalization, our method builds on the fact that linear and planar surfaces contain a high level of redundancy propagated into iterative estimation pipelines. We define the concept of gradient flow quantifying the surface underlying a point. We also show that maximizing the entropy of the gradient flow minimizes point redundancy for robot ego-motion estimation. We integrate the proposed method into the point-based KISS-ICP and feature-based LOAM odometry pipelines and evaluate it experimentally on KITTI, Hilti-Oxford, and custom datasets from multirotor UAVs. The experiments show that the proposed sampling technique outperforms state-of-the-art methods in well-conditioned as well as in geometrically-degenerated settings, in both accuracy and speed.

翻译：摘要：移动机器人状态估计中常用的点云采样方法存在较高的点冗余度。这种点冗余会降低估计管线的处理速度，并可能导致在几何对称或无结构环境中实时估计发生漂移。本文提出一种新型点云采样方法，通过最小化云内冗余度来降低几何退化效应的影响。该方法是对传统稀疏化方法的替代——传统方法通过归一化点密度来满足机器人实时性能约束。与密度归一化不同，本文方法基于以下发现：线性平面和曲面在迭代估计管线中会传播高度冗余。我们定义了量化点表面梯度的梯度流概念，并证明最大化梯度流熵可最小化机器人自运动估计中的点冗余。我们将所提方法集成到基于点的KISS-ICP和基于特征的LOAM里程计管线中，并在KITTI、Hilti-Oxford数据集以及多旋翼无人机自采数据集上进行了实验验证。实验结果表明，在良态与几何退化场景下，该采样技术在精度和速度两方面均优于现有方法。