Abstract Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given people’s orientations, mobile systems can apply navigation strategies which take people’s proxemics into account or approach them in a human like manner to perform human robot interaction (HRI) tasks. In this paper, we present an approach for person orientation estimation based on computationally efficient features extracted from colored point clouds, formerly used for a two-class person attribute classification. The classification approach has been extended to the continuous domain while treating the problem of orientation estimation in real time. Furthermore, we present an approach for tracking estimated orientations over time using a Bayesian filter. We will show that tracking can increase the accuracy of orientations by up to 3 . 69 ° on a dataset recorded with a mobile robot. Best results on this highly challenging dataset are achieved with a regression approach for orientation estimation in combination with tracking. The mean angular error of just 16 . 49 ° proofs the applicability in real-world scenarios.

中文翻译：

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使用彩色点云进行实时人物方向估计和跟踪

摘要 稳健地估计人的方向是广泛应用的关键先决条件。特别是对于在人口稠密环境中运行的自主系统，一个人的方向可以提供有价值的信息，以提高他们的接受度。给定人们的方向，移动系统可以应用导航策略，将人们的近义词考虑在内或以类似人类的方式接近他们来执行人机交互 (HRI) 任务。在本文中，我们提出了一种基于从彩色点云中提取的计算高效特征的人物方向估计方法，该特征以前用于二类人物属性分类。在实时处理方向估计问题的同时，分类方法已扩展到连续域。此外，我们提出了一种使用贝叶斯滤波器随时间跟踪估计方向的方法。我们将证明跟踪可以将方向的准确性提高多达 3 。移动机器人记录的数据集上的 69°。通过结合跟踪的方向估计回归方法，在这个极具挑战性的数据集上获得了最佳结果。平均角度误差仅为 16 。49°证明了在现实世界场景中的适用性。