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.

翻译：本文提出一种新颖的实时平面语义地图构建算法，专为人形机器人在复杂地形（如楼梯）中的导航而设计。该方法可适配任意里程计输入，并利用GPU加速的平面提取流程，实现全局一致语义地图的快速生成。我们在深度图像上采用各向异性扩散滤波器，有效抑制梯度跳跃产生的噪声，同时保持关键边缘细节，从而提升法向量图像的精度与平滑度。各向异性扩散与基于RANSAC的平面提取过程均针对GPU并行处理进行优化，显著提升了计算效率。本方法实现了实时处理性能，单帧处理速率超过$30~Hz$，能够快速高效地完成精细平面提取与地图管理。大量测试验证了算法在实时场景中的效能，并展示了其在人形机器人步态规划中的实际应用，显著提升了机器人在动态环境中的导航能力。