The typical point cloud sampling methods used in state estimation for mobile robots preserve a high level of point redundancy. This redundancy unnecessarily slows down the estimation pipeline and may cause drift under real-time constraints. Such undue latency becomes a bottleneck for resource-constrained robots (especially UAVs), requiring minimal delay for agile and accurate operation. We propose a novel, deterministic, uninformed, and single-parameter point cloud sampling method named RMS that minimizes redundancy within a 3D point cloud. In contrast to the state of the art, RMS balances the translation-space observability by leveraging the fact that linear and planar surfaces inherently exhibit high redundancy propagated into iterative estimation pipelines. We define the concept of gradient flow, quantifying the local 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 RMS into the point-based KISS-ICP and feature-based LOAM odometry pipelines and evaluate experimentally on KITTI, Hilti-Oxford, and custom datasets from multirotor UAVs. The experiments demonstrate that RMS outperforms state-of-the-art methods in speed, compression, and accuracy in well-conditioned as well as in geometrically-degenerated settings.

翻译：移动机器人的状态估计中，典型的点云采样方法保留了较高水平的点冗余。这种冗余不必要地减慢了估计流程的速度，并可能在实时约束下导致漂移。这种不当的延迟成为资源受限机器人（尤其是无人机）的瓶颈，这些机器人需要最小延时以实现敏捷和精确的操作。我们提出了一种新颖的、确定性的、无信息输入的、单参数的点云采样方法，名为RMS，旨在最小化三维点云中的冗余。与现有技术相比，RMS通过利用线性和平面表面本质上会传播高冗余这一事实（该冗余会进入迭代估计流程），来平衡平移空间的可观测性。我们定义了梯度流的概念，用于量化点下方局部表面的性质。我们还证明，最大化梯度流的熵可以最小化机器人自身运动估计中的点冗余。我们将RMS集成到基于点的KISS-ICP和基于特征的LOAM里程计流程中，并在KITTI、Hilti-Oxford以及来自多旋翼无人机的自定义数据集上进行了实验评估。实验结果表明，无论在条件良好还是几何退化场景中，RMS在速度、压缩率和精度方面均优于现有方法。