by Oscar Mendez, Simon Hadfield, Nicolas Pugeault and Richard Bowden
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 ro- bust semantic understanding. Leveraging this semantic vi- sion 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 fore- front 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 be- cause 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 seman- tically 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.
Reference:
SeDAR: Reading floorplans like a human Using Deep Learning to enable human-inspired localisation (Oscar Mendez, Simon Hadfield, Nicolas Pugeault and Richard Bowden), In Springer International Journal of Computer Vision (IJCV), volume 128, 2019.
Bibtex Entry:
@Article{Mendez19,
Title = {SeDAR: Reading floorplans like a human Using Deep Learning to enable human-inspired localisation},
author = {Oscar Mendez and Simon Hadfield and Nicolas Pugeault and Richard Bowden},
Journal = {Springer International Journal of Computer Vision (IJCV)},
Year = {2019},
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 ro-
bust semantic understanding. Leveraging this semantic vi-
sion 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 fore-
front 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 be-
cause 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 seman-
tically 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.},
Url = {http://personalpages.surrey.ac.uk/s.hadfield/papers/Mendez19.pdf},
Volume = {128},
Issue = {5},
Pages = {1286--1310},
}