SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation
SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation
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SeDAR：像人类一样阅读平面图——利用深度学习实现以人为本的本地化
DOI：
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发表时间：
2019
影响因子：
19.5
通讯作者：
R. Bowden
中科院分区：
文献类型：
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作者：
Oscar Alejandro Mendez Maldonado;Simon Hadfield;N. Pugeault;R. Bowden
使用人类级别的语义信息来帮助机器人完成任务，最近已经成为计算机视觉和机器人学的一个重要领域。这得益于深度学习的进步，它允许一致和强大的语义理解。利用这种语义世界观，人类层面的理解可以从许多不同的方法中自然产生。特别是，使用语义信息来帮助定位和重建一直处于这两个领域的最前沿。像机器人一样，人类也需要在结构中定位的能力。为了帮助这一点，人类设计了我们的结构的高级语义图，称为平面图。我们非常擅长在其中进行本地化，即使对机器人使用的深度信息的访问有限。这是因为我们关注的是语义元素的分布，而不是几何元素。这方面的证据是，人类通常能够在没有适当缩放的平面图中定位。为了赋予机器人这种能力，有必要使用本地化方法，利用人类使用的相同语义信息。在本文中，我们提出了一种新的语义全局定位方法。我们的方法依赖于布局图中的语义标签。利用深度学习从RGB图像中提取语义标签，并将其与平面图进行比较以进行定位。虽然我们的方法能够使用范围测量（如果可用），但我们证明它们是不必要的，因为我们可以在没有它们的情况下实现与最先进技术相当的结果。
The use of human-level semantic information to aid robotic tasks has recently become an important area for both Computer Vision and Robotics. This has been enabled by advances in Deep Learning that allow consistent and robust semantic understanding. Leveraging this semantic vision of the world has allowed human-level understanding to naturally emerge from many different approaches. Particularly, the use of semantic information to aid in localisation and reconstruction has been at the forefront of both fields. Like robots, humans also require the ability to localise within a structure. To aid this, humans have designed high-level semantic maps of our structures called floorplans. We are extremely good at localising in them, even with limited access to the depth information used by robots. This is because we focus on the distribution of semantic elements, rather than geometric ones. Evidence of this is that humans are normally able to localise in a floorplan that has not been scaled properly. In order to grant this ability to robots, it is necessary to use localisation approaches that leverage the same semantic information humans use. In this paper, we present a novel method for semantically enabled global localisation. Our approach relies on the semantic labels present in the floorplan. Deep Learning is leveraged to extract semantic labels from RGB images, which are compared to the floorplan for localisation. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.