DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

Le Gentil, Cedric and Vayugundla, Mallikarjuna and Giubilato, Riccardo and Vidal-Calleja, Teresa and Triebel, Rudolph (2020) Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). International Conference on Intelligent Robots and Systems (IROS), Virtual.

[img] PDF


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.

Item URL in elib:https://elib.dlr.de/137981/
Document Type:Conference or Workshop Item (Speech)
Title:Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Le Gentil, Cedriccedric.legentil (at) student.uts.edu.auUNSPECIFIED
Vayugundla, MallikarjunaMallikarjuna.Vayugundla (at) dlr.dehttps://orcid.org/0000-0002-9277-0461
Giubilato, RiccardoRiccardo.Giubilato (at) dlr.dehttps://orcid.org/0000-0002-3161-3171
Vidal-Calleja, Teresateresa.vidalcalleja (at) uts.edu.auUNSPECIFIED
Triebel, RudolphRudolph.Triebel (at) dlr.dehttps://orcid.org/0000-0002-7975-036X
Journal or Publication Title:2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:Localization; Space Robotics and Automation;Multi-Modal Perception; Visual-Based Navigation
Event Title:International Conference on Intelligent Robots and Systems (IROS)
Event Location:Virtual
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Multisensory World Modelling (E3D OS) [SY], R - Robotic Science Explorer
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Vayugundla, Mallikarjuna
Deposited On:26 Nov 2020 09:52
Last Modified:26 Nov 2020 09:52

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.