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SeDAR: Reading floorplans like a human

Mendez, Oscar, Hadfield, Simon, Pugeault, Nicolas and Bowden, Richard (2019) SeDAR: Reading floorplans like a human International Journal of Computer Vision.

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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 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 highlevel 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Mendez, Oscaro.mendez@surrey.ac.uk
Hadfield, Simons.hadfield@surrey.ac.uk
Pugeault, NicolasN.Pugeault@surrey.ac.uk
Bowden, RichardR.Bowden@surrey.ac.uk
Date : 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC), Innovate UK
Copyright Disclaimer : © Springer Science+Business Media, LLC, part of Springer Nature 2019
Related URLs :
Depositing User : Clive Harris
Date Deposited : 06 Nov 2019 08:43
Last Modified : 06 Nov 2019 08:43
URI: http://epubs.surrey.ac.uk/id/eprint/853051

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