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.

提出了一种新的点云采样方法，通过降低点云中的冗余，减少在几何对称和无结构环境下实时估计漂移的影响。该方法在机器人的几何运动估计中最大化梯度流的熵，从而最小化点的冗余。实验结果表明，该采样技术在良好条件和几何退化环境下的准确度和速度优于现有方法。