论文标题
使用空间感知序列的轨迹注释
Trajectory annotation using sequences of spatial perception
论文作者
论文摘要
在不久的将来，越来越多的机器将在人类空间附近执行任务，或直接在其空间绑定的活动中支持它们。为了简化机器人单位和/或人类之间的口头交流以及可靠且健壮的系统W.R.T.需要噪声和处理结果。这项工作为解决此任务建立了基础。通过在从轨迹数据中学到的内饰中使用对空间感知的连续表示，我们的方法群集依赖于其空间上下文。我们提出了一种基于神经自动编码的无监督学习方法，该方法学习了语义上有意义的时空轨迹数据的连续编码。该学到的编码可用于形成原型表示。我们提出了有希望的结果，可以清除未来应用的道路。
In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the verbal communication and the interaction between robotic units and/or humans, reliable and robust systems w.r.t. noise and processing results are needed. This work builds a foundation to address this task. By using a continuous representation of spatial perception in interiors learned from trajectory data, our approach clusters movement in dependency to its spatial context. We propose an unsupervised learning approach based on a neural autoencoding that learns semantically meaningful continuous encodings of spatio-temporal trajectory data. This learned encoding can be used to form prototypical representations. We present promising results that clear the path for future applications.
