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

Multi-Resolution Planar Region Extraction for Uneven Terrains

Enabling bipedal walking robots to learn how to maneuver over highly uneven,
dynamically changing terrains is challenging due to the complexity of robot
dynamics and interacted environments. Recent advancements in learning from
demonstrations have shown promising results for robot learning in complex
environments. While imitation learning of expert policies has been
well-explored, the study of learning expert reward functions is largely
under-explored in legged locomotion. This paper brings state-of-the-art Inverse
Reinforcement Learning (IRL) techniques to solving bipedal locomotion problems
over complex terrains. We propose algorithms for learning expert reward
functions, and we subsequently analyze the learned functions. Through nonlinear
function approximation, we uncover meaningful insights into the expert's
locomotion strategies. Furthermore, we empirically demonstrate that training a
bipedal locomotion policy with the inferred reward functions enhances its
walking performance on unseen terrains, highlighting the adaptability offered
by reward learning.

通过逆强化学习 (IRL) 技术解决复杂地形上的双足机器人行走问题，并提出用于学习专家奖励函数的算法，通过非线性函数逼近揭示专家的运动策略，同时通过训练推断奖励函数，提高双足机器人在未知地形上的行走性能。