让机器人实时看清楼梯并安全攀爬
Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing
- 用GPU加速处理深度图，快速提取平面信息
- 每秒处理超30帧，实现毫秒级地图更新
- 适合需要精准地形感知的仿人机器人
我们提出一种专为仿人机器人在复杂地形（如楼梯）中导航设计的实时平面语义映射算法。该方法可适配任意里程计输入，利用GPU加速进行平面提取，实现全局一致的语义地图快速生成。通过在深度图上应用各向异性扩散滤波器，有效抑制梯度突变带来的噪声，同时保留关键边缘细节，提升法向量图像的精度与平滑性。各向异性扩散与基于RANSAC的平面提取过程均针对GPU并行计算优化，显著提升计算效率。本方法实现真正实时性能，单帧处理速率超过30 Hz，能够迅速完成精细平面提取与地图管理。大量测试验证了该算法在实时场景中的能力，并证明其在仿人机器人步态规划中的实用价值，显著增强其在动态环境中的导航能力。
原文摘要 · Abstract (English)
We present a novel algorithm for real-time planar semantic mapping tailored for humanoid robots navigating complex terrains such as staircases. Our method is adaptable to any odometry input and leverages GPU-accelerated processes for planar extraction, enabling the rapid generation of globally consistent semantic maps. We utilize an anisotropic diffusion filter on depth images to effectively minimize noise from gradient jumps while preserving essential edge details, enhancing normal vector images' accuracy and smoothness. Both the anisotropic diffusion and the RANSAC-based plane extraction processes are optimized for parallel processing on GPUs, significantly enhancing computational efficiency. Our approach achieves real-time performance, processing single frames at rates exceeding $30~Hz$, which facilitates detailed plane extraction and map management swiftly and efficiently. Extensive testing underscores the algorithm's capabilities in real-time scenarios and demonstrates its practical application in humanoid robot gait planning, significantly improving its ability to navigate dynamic environments.
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