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InFuse Data Fusion Methodology for Space Robotics, Awareness and Machine Learning

Post, Mark and Michalec, Romain and Bianco, Alessandro and Yan, Xiu and De Maio, Andrea and Labourey, Quentin and Lacroix, Simon and Gancet, Jeremi and Govindaraj, Shashank and Martinez-Gonzalez, Xavier and Dalati, Iyas and Domínguez, Raúl and Wehbe, Bilal and Fabisch, Alexander and Röhrig, Enno and Souvannavog, Fabrice and Bissonnette, Vincent and Smisek, Michal and Oumer, Nassir and Meyer, Lukas and Triebel, Rudolph and Marton, Zoltan-Csaba (2018) InFuse Data Fusion Methodology for Space Robotics, Awareness and Machine Learning. In: Proceedings of the International Astronautical Congress, IAC. HAL. 69th International Astronautical Congress, Oct 2018, Bremen, Germany.

Full text not available from this repository.

Official URL: https://hal.laas.fr/hal-02092238

Abstract

Autonomous space vehicles such as orbital servicing satellites and planetary exploration rovers must be comprehensively aware of their environment in order to make appropriate decisions. Multi-sensor data fusion plays a vital role in providing these autonomous systems with sensory information of different types, from different locations, and at different times. The InFuse project, funded by the European Commission's Horizon 2020 Strategic Research Cluster in Space Robotics, provides the space community with an open-source Common Data Fusion Framework (CDFF) by which data may be fused in a modular fashion from multiple sensors. In this paper, we summarize the modular structure of this CDFF and show how it is used for the processing of sensor data to obtain data products for both planetary and orbital space robotic applications. Multiple sensor data from field testing that includes inertial measurements, stereo vision, and scanning laser range information is first used to produce robust multi-layered environmental maps for path planning. This information is registered and fused within the CDFF to produce comprehensive three-dimensional maps of the environment. To further explore the potential of the CDFF, we illustrate several applications of the CDFF that have been evaluated for orbital and planetary use cases of environmental reconstruction, mapping, navigation, and visual tracking. Algorithms for learning of maps, outlier detection, localization, and identification of objects are available within the CDFF and some early results from their use in space analogue scenarios are presented. These applications show how the CDFF can be used to provide a wide variety of data products for use by awareness and machine learning processes in space robots.

Item URL in elib:https://elib.dlr.de/132983/
Document Type:Conference or Workshop Item (Other)
Title:InFuse Data Fusion Methodology for Space Robotics, Awareness and Machine Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Post, MarkUniversity of StrathclydeUNSPECIFIED
Michalec, RomainUniversity of StrathclydeUNSPECIFIED
Bianco, AlessandroUniversity of StrathclydeUNSPECIFIED
Yan, XiuUniversity of StrathclydeUNSPECIFIED
De Maio, AndreaLAAS-CNRSUNSPECIFIED
Labourey, QuentinLAAS-CNRSUNSPECIFIED
Lacroix, SimonLAAS-CNRSUNSPECIFIED
Gancet, JeremiSpace Applications ServicesUNSPECIFIED
Govindaraj, ShashankSpace Applications ServicesUNSPECIFIED
Martinez-Gonzalez, XavierSpace Applications ServicesUNSPECIFIED
Dalati, IyasSpace Applications ServicesUNSPECIFIED
Domínguez, RaúlDFKIUNSPECIFIED
Wehbe, BilalDFKIUNSPECIFIED
Fabisch, AlexanderDFKIUNSPECIFIED
Röhrig, EnnoDFKIUNSPECIFIED
Souvannavog, FabriceMagellium SASUNSPECIFIED
Bissonnette, VincentMagellium SASUNSPECIFIED
Smisek, MichalMichal.Smisek (at) dlr.deUNSPECIFIED
Oumer, NassirNassir.Oumer (at) dlr.deUNSPECIFIED
Meyer, LukasLukas.Meyer (at) dlr.dehttps://orcid.org/0000-0001-9514-8494
Triebel, RudolphRudolph.Triebel (at) dlr.dehttps://orcid.org/0000-0002-7975-036X
Marton, Zoltan-CsabaZoltan.Marton (at) dlr.dehttps://orcid.org/0000-0002-3035-493X
Date:2018
Journal or Publication Title:Proceedings of the International Astronautical Congress, IAC
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Publisher:HAL
Status:Published
Keywords:Sensor Fusion, Rover, Space Robotics, Classification
Event Title:69th International Astronautical Congress
Event Location:Bremen, Germany
Event Type:international Conference
Event Dates:Oct 2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Marton, Dr. Zoltan-Csaba
Deposited On:18 Dec 2019 13:39
Last Modified:18 Dec 2019 13:39

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