Unsteady locomotion and the dynamic environment are two problems that block humanoid robots to apply visual Simultaneous Localization and Mapping (SLAM) approaches. Humans are often considered as moving obstacles and targets in humanoid robots working space. Thus, in this paper, we propose a robust dense RGB-D SLAM approach for the humanoid robots working in the dynamic human environments. To deal with the dynamic human objects, a deep learning-based human detector is combined in the proposed method. After the removal of the dynamic object, we fast reconstruct the static environments through a dense RGB-D point clouds fusion framework. In addition to the humanoid robot falling problem, which usually results in visual sensing discontinuities, we propose a novel point clouds registration-based method to relocate the robot pose. Therefore, our robot can continue the self localization and mapping after the falling. Experimental results on both the public benchmarks and the real humanoid robot SLAM experiments indicated that the proposed approach outperformed state-of-the-art SLAM solutions in dynamic human environments.

中文翻译：

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动态人类环境中的人形机器人 RGB-D SLAM

不稳定的运动和动态环境是阻碍类人机器人应用视觉同步定位和映射 (SLAM) 方法的两个问题。人通常被认为是人形机器人工作空间中移动的障碍物和目标。因此，在本文中，我们为在动态人类环境中工作的类人机器人提出了一种稳健的密集 RGB-D SLAM 方法。为了处理动态的人体对象，在所提出的方法中结合了基于深度学习的人体检测器。去除动态对象后，我们通过密集的 RGB-D 点云融合框架快速重建静态环境。除了通常会导致视觉感知不连续的类人机器人坠落问题外，我们还提出了一种新的基于点云配准的机器人姿态重定位方法。所以，我们的机器人可以在跌倒后继续进行自我定位和建图。公共基准和真实人形机器人 SLAM 实验的实验结果表明，所提出的方法在动态人类环境中优于最先进的 SLAM 解决方案。