SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation
Mendez, O; Hadfield, S; Pugeault, N; et al.Bowden, R
Date: 28 September 2019
Article
Journal
International Journal of Computer Vision
Publisher
Springer Verlag
Publisher DOI
Abstract
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 ...
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.
Computer Science
Faculty of Environment, Science and Economy
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