PolygMap：用于人形机器人爬楼梯的感知式运动框架
PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing
AI总结：
本文提出PolygMap框架，通过多传感器融合构建多边形楼梯平面语义地图，在NVIDIA Orin上实现20-30Hz全身运动规划，经真实场景实验验证可高效鲁棒地完成人形机器人爬楼梯任务。
摘要：
近来，双足机器人行走技术已取得显著发展，主要围绕常规行走方案展开。为模拟人类行走，机器人需在未知空间中精准踏至所观测的位置。本文提出PolygMap，一种用于人形机器人爬楼梯的感知式运动规划框架。核心思路是构建实时多边形楼梯平面语义地图，再利用这些多边形平面段规划足步步态。平面分割与视觉里程计通过多传感器融合（LiDAR、RGB-D相机及IMUs）实现。该框架部署于NVIDIA Orin，可输出20-30Hz的全身运动规划。室内及室外真实场景实验表明，所提方法在人形机器人爬楼梯任务中兼具高效性与鲁棒性。
英文摘要：
Recently, biped robot walking technology has been significantly developed, mainly in the context of a bland walking scheme. To emulate human walking, robots need to step on the positions they see in unknown spaces accurately. In this paper, we present PolyMap, a perception-based locomotion planning framework for humanoid robots to climb stairs. Our core idea is to build a real-time polygonal staircase plane semantic map, followed by a footstep planar using these polygonal plane segments. These plane segmentation and visual odometry are done by multi-sensor fusion(LiDAR, RGB-D camera and IMUs). The proposed framework is deployed on a NVIDIA Orin, which performs 20-30 Hz whole-body motion planning output. Both indoor and outdoor real-scene experiments indicate that our method is efficient and robust for humanoid robot stair climbing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
