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：当前用于移动机器人状态估计的典型点云采样方法保持了较高的点冗余度。这种冗余会不必要地拖慢估计流程，并可能在实时约束下导致漂移。这种不当延迟成为资源受限机器人（尤其是无人机）的瓶颈，需要最小延迟以实现敏捷且精确的操作。我们提出一种新颖、确定性、无信息且单参数的点云采样方法RMS，该方法能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身在迭代估计流程中会传递高冗余度的特性，平衡了平移空间的可观测性。我们定义了梯度流的概念，量化点下方局部表面特性。同时，我们证明最大化梯度流的熵能最小化机器人自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以及多旋翼无人机自定义数据集上进行实验评估。实验表明，在常规条件及几何退化场景下，RMS在速度、压缩率和精度方面均优于现有方法。