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.

本文研究从无序点云测量中提取不平坦地形中的平面区域的问题，并提出了一种多分辨率平面区域提取策略，解决了现有方法在边界精度和计算效率方面存在的问题。通过合成和实际世界实验验证了该方法的效率和鲁棒性，证明了该方法在各种不平坦地形中能够有效泛化并保持实时性能，达到超过35 FPS的帧率。

多分辨率不平地区域提取