| 摘 要： |
To ensure safe and smooth human-robot interaction, autonomous robots operating around people often necessitate 3D human body orientation and bounding box estimation. An orientation of the human is generally defined by the motion around the yaw-axis. However, human-robot interaction tasks may necessitate different orientation definitions depending on the application, for instance an orientation can be defined using shoulder and hip joints. A 3D skeleton map, consisting of multiple 3D body joint coordinates, enables defining an orientation in diverse ways. However, estimating 3D skeleton maps from monocular images is computationally expensive. Existing approaches using large neural network models are impractical for real-time robot operation due to the limited onboard computation and power resources. In this paper, we automatically label the 3D human body orientation from 3D skeleton maps, and present a deep learning method to estimate 3D orientation and bounding box. We achieve this by leveraging a larger neural perception model to automatically generate an annotated training dataset, using a functional mapping from 3D skeleton joint coordinates to the defined orientation and bounding boxes. Experimental results demonstrate that our perception model estimates 3D human body orientation with an average error of 10.94?around the yaw-axis and the 3D bounding box with errors of 19.15% in width, 8.71% in length, and 4.71% in height compared to the annotator's output, while doubling the FPS rate.
为确保人机交互的安全与顺畅，围绕人类活动的自主机器人通常需要进行三维人体姿态和包围框估计。人体的姿态一般通过绕偏航轴（yaw-axis）的运动来定义。然而，不同应用场景下的人机交互任务可能需要不同的姿态定义方式，例如可采用肩部和髋关节来定义姿态。由多个三维人体关节坐标构成的三维骨架图，能够以多种方式定义姿态。但从单目图像中估计三维骨架图计算成本较高。现有使用大型神经网络模型的方法由于车载计算资源和电力限制，难以实现实时机器人操作。本文提出一种自动标注三维人体姿态的方法，并利用深度学习技术估计三维姿态和包围框。我们通过使用一个更大型的神经感知模型，自动生成带标注的训练数据集，该模型利用从三维骨架关节坐标到定义方向和边界框之间的函数映射。实验结果表明，我们的感知模型在估计人体三维姿态时，相对于标注者输出的平均误差约为10.94度（围绕偏航轴），三维边界框的宽度、长度和高度误差分别为19.15%、8.71%和4.71%，同时将帧率提高了两倍。 |
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