让机器人像人一样看路爬楼梯，实时构建台阶地图并规划脚步。
PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing
- 融合激光雷达、深度相机和惯性传感器，实时生成台阶的多边形语义地图。
- 在真实室内外环境中实现20-30赫兹的全身运动规划，稳定爬楼。
- 适合需要复杂地形适应能力的人形机器人研究与开发人员。
近年来，双足机器人行走技术取得了显著进展，主要集中在平坦地面的行走方案上。为模拟人类行走，机器人需在未知空间中准确踏步于所见位置。本文提出PolyMap，一种基于感知的人形机器人爬楼梯运动规划框架。核心思想是实时构建多边形阶梯平面语义地图，并基于这些平面段进行步态规划。平面分割与视觉里程计通过多传感器融合（激光雷达、RGB-D相机和惯性测量单元）实现。该框架部署在NVIDIA Orin平台上，可输出20-30赫兹的全身运动规划结果。室内与室外真实场景实验表明，本方法在人形机器人爬楼梯任务中高效且鲁棒。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。