  This paper studies the problem of extracting planar regions in uneven
terrains from unordered point cloud measurements. Such a problem is critical in
various robotic applications such as robotic perceptive locomotion. While
existing approaches have shown promising results in effectively extracting
planar regions from the environment, they often suffer from issues such as low
computational efficiency or loss of resolution. To address these issues, we
propose a multi-resolution planar region extraction strategy in this paper that
balances the accuracy in boundaries and computational efficiency. Our method
begins with a pointwise classification preprocessing module, which categorizes
all sampled points according to their local geometric properties to facilitate
multi-resolution segmentation. Subsequently, we arrange the categorized points
using an octree, followed by an in-depth analysis of nodes to finish
multi-resolution plane segmentation. The efficiency and robustness of the
proposed approach are verified via synthetic and real-world experiments,
demonstrating our method's ability to generalize effectively across various
uneven terrains while maintaining real-time performance, achieving frame rates
exceeding 35 FPS.
