论文标题
没有完美的户外活动：深入介绍基于GNSS的位置上下文
No Perfect Outdoors: Towards A Deep Profiling of GNSS-based Location Contexts
论文作者
论文摘要
虽然室外和室内定位方法都在蓬勃发展，但如何正确地嫁给它们以在城市地区提供普遍的本地化性仍然开放。最近，关于室内室外检测的建议迈出了这种整合但复杂的城市环境的第一步，使这种二进制分类变得无能。在本文中，我们打算充分探索RAW GNSS测量值，以更好地描述多元化的城市环境。从本质上讲，我们应对复杂GNSS数据引入的挑战，并采用深度学习模型来确定各个位置环境的表示。我们进一步开发了我们深入分析的两个初步应用。一方面，与二进制室内室外检测相比，我们提供的语义分类更细。另一方面，我们得出了比Google Maps提供的GPS错误指标更有意义。我们的广泛数据收集和痕量驱动的评估都证实了这些结果。
While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently proposals on indoor-outdoor detection make the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. In this paper, we intend to fully explore raw GNSS measurements in order to better characterize the diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling. On one hand, we offer a more fine-grained semantic classification than binary indoor-outdoor detection. On the other hand, we derive a GPS error indicator more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.
