Gaussian Process Gradient Maps for Loop-Closure Detection in
Unstructured Planetary Environments
- URL: http://arxiv.org/abs/2009.00221v1
- Date: Tue, 1 Sep 2020 04:41:40 GMT
- Title: Gaussian Process Gradient Maps for Loop-Closure Detection in
Unstructured Planetary Environments
- Authors: Cedric Le Gentil, Mallikarjuna Vayugundla, Riccardo Giubilato,
Wolfgang St\"urzl, Teresa Vidal-Calleja, Rudolph Triebel
- Abstract summary: The ability to recognize previously mapped locations is an essential feature for autonomous systems.
Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain.
This paper presents a method to solve the loop closure problem using only spatial information.
- Score: 17.276441789710574
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ability to recognize previously mapped locations is an essential feature
for autonomous systems. Unstructured planetary-like environments pose a major
challenge to these systems due to the similarity of the terrain. As a result,
the ambiguity of the visual appearance makes state-of-the-art visual place
recognition approaches less effective than in urban or man-made environments.
This paper presents a method to solve the loop closure problem using only
spatial information. The key idea is to use a novel continuous and
probabilistic representations of terrain elevation maps. Given 3D point clouds
of the environment, the proposed approach exploits Gaussian Process (GP)
regression with linear operators to generate continuous gradient maps of the
terrain elevation information. Traditional image registration techniques are
then used to search for potential matches. Loop closures are verified by
leveraging both the spatial characteristic of the elevation maps (SE(2)
registration) and the probabilistic nature of the GP representation. A
submap-based localization and mapping framework is used to demonstrate the
validity of the proposed approach. The performance of this pipeline is
evaluated and benchmarked using real data from a rover that is equipped with a
stereo camera and navigates in challenging, unstructured planetary-like
environments in Morocco and on Mt. Etna.
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