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The typical point cloud sampling methods used in state estimation for mobilerobots preserve a high level of point redundancy. The point redundancy slowsdown the estimation pipeline and can make real-time estimation drift ingeometrically symmetrical and structureless environments. We propose a novelpoint cloud sampling method that is capable of lowering the effects ofgeometrical degeneracies by minimizing redundancy within the cloud. Theproposed method is an alternative to the commonly used sparsification methodsthat normalize the density of points to comply with the constraints on thereal-time capabilities of a robot. In contrast to density normalization, ourmethod builds on the fact that linear and planar surfaces contain a high levelof redundancy propagated into iterative estimation pipelines. We define theconcept of gradient flow quantifying the surface underlying a point. We alsoshow that maximizing the entropy of the gradient flow minimizes pointredundancy for robot ego-motion estimation. We integrate the proposed methodinto the point-based KISS-ICP and feature-based LOAM odometry pipelines andevaluate it experimentally on KITTI, Hilti-Oxford, and custom datasets frommultirotor UAVs. The experiments show that the proposed sampling techniqueoutperforms state-of-the-art methods in well-conditioned as well as ingeometrically-degenerated settings, in both accuracy and speed.

RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose Estimation in Degenerated Environments