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Robust place recognition with Gaussian Process Gradient Maps for teams of robotic explorers in challenging lunar environments

Giubilato, Riccardo and Vayugundla, Mallikarjuna and Le Gentil, Cedric and Schuster, Martin J. and McDonald, William and Vidal-Calleja, Teresa and Wedler, Armin and Triebel, Rudolph (2022) Robust place recognition with Gaussian Process Gradient Maps for teams of robotic explorers in challenging lunar environments. In: Proceedings of the International Astronautical Congress, IAC. International Astronautical Congress - IAC 2022, Paris, France. ISSN 0074-1795.

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Official URL: https://iafastro.directory/iac/paper/id/69374/summary/

Abstract

Teams of mobile robots will play a key role towards future planetary exploration missions. In fact, plans for upcoming lunar exploration, and other extraterrestrial bodies, foresee an extensive usage of robots for the purposes of in-situ analysis, building infrastructure and realizing maps of the environment for its exploitation. To enable prolonged robotic autonomy, however, it is critical for the robotic agents to be able to robustly localize themselves during their motion and, concurrently, to produce maps of the environment. To this end, visual SLAM (Simultaneous Localization and Mapping) techniques have been developed during the years and found successful application in several terrestrial fields, such as autonomous driving, automated construction and agricultural robotics. To this day, autonomous navigation has been demonstrated in various robotic missions to Mars, e.g., from NASA's Mars Exploration Rover (MER) Missions, to NASA's Mars Science Laboratory (Curiosity) and the current Mars2020 Perseverance, thanks to the implementation of Visual Odometry, using cameras to robustly estimate the rover's ego-motion. While VO techniques enable the traversal of large distances from one scientific target to the other, future operations, e.g., for building or maintenance of infrastructure, will require robotic agents to repeatedly visit the same environment. In this case, the ability to re-localize themselves with respect to previously visited places, and therefore the ability to create consistent maps of the environment, is paramount to achieve localization accuracies, that are far above what is achievable from global localization approaches. The planetary environment, however, poses significant challenges to this goal, due to extreme lighting conditions, severe visual aliasing and a lack of uniquely identifiable natural "features". For this reason, we developed an approach for re-localization and place recognition, that relies on Gaussian Processes, to efficiently represent portions of the local terrain elevation, named "GPGMaps" (Gaussian Process Gradient Maps), and to use its gradient in conjunction with traditional visual matching techniques. In this paper, we demonstrate, analyze and report the performances of our SLAM approach, based on GPGMaps, during the 2022 ARCHES (Autonomous Robotic Networks to Help Modern Societies) mission, that took place on the volcanic ash slopes of Mt. Etna, Sicily, a designated planetary analogous environment. The proposed SLAM system has been deployed for real-time usage on a robotic team that includes the LRU (Lightweight Rover Unit), a planetary-like rover with high autonomy, perceptual and locomotion capabilities, to demonstrate enabling technologies for future lunar applications.

Item URL in elib:https://elib.dlr.de/189217/
Document Type:Conference or Workshop Item (Poster)
Title:Robust place recognition with Gaussian Process Gradient Maps for teams of robotic explorers in challenging lunar environments
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Giubilato, RiccardoRiccardo.Giubilato (at) dlr.dehttps://orcid.org/0000-0002-3161-3171
Vayugundla, MallikarjunaMallikarjuna.Vayugundla (at) dlr.dehttps://orcid.org/0000-0002-9277-0461
Le Gentil, Cedriccedric.legentil (at) uts.edu.auhttps://orcid.org/0000-0002-9790-5935
Schuster, Martin J.martin.schuster (at) dlr.dehttps://orcid.org/0000-0002-6983-3719
McDonald, Williamwilliam.t.mcdonald (at) student.uts.edu.auUNSPECIFIED
Vidal-Calleja, Teresateresa.vidalcalleja (at) uts.edu.auhttps://orcid.org/0000-0002-5763-9644
Wedler, Arminarmin.wedler (at) dlr.dehttps://orcid.org/0000-0001-8641-0163
Triebel, Rudolphrudolph.triebel (at) dlr.dehttps://orcid.org/0000-0002-7975-036X
Date:October 2022
Journal or Publication Title:Proceedings of the International Astronautical Congress, IAC
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
ISSN:0074-1795
Status:Published
Keywords:Field Robotics, Space Robotics, Demo mission, Machine learning, SLAM
Event Title:International Astronautical Congress - IAC 2022
Event Location:Paris, France
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Planetary Exploration
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Giubilato, Riccardo
Deposited On:19 Oct 2022 16:01
Last Modified:19 Oct 2022 16:01

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