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Viewpoint Selection for Rover Relative Pose Estimation Driven by Minimal Uncertainty Criteria

Chiodini, Sebastiano and Giubilato, Riccardo and Pertile, Marco and Salvioli, Federico and Bussi, Diego and Barrera, Marco and Franceschetti, Paola and Debei, Stefano (2021) Viewpoint Selection for Rover Relative Pose Estimation Driven by Minimal Uncertainty Criteria. IEEE Transactions on Instrumentation and Measurement, 70. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TIM.2021.3117089. ISSN 0018-9456.

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Official URL: https://ieeexplore.ieee.org/document/9555599


Pose estimation is critical for mobile robots to fulfill various tasks, such as path following or mapping the environment. This is usually accomplished by simultaneous localization and mapping (SLAM). However, computationally constrained systems, such as planetary rovers, rely on less intensive guidance navigation and control (GNC) solutions generally derived solely from visual odometry (VO), wheel odometry, and the onboard inertial measurement unit. Although providing adequate localization performances, the drift accumulated overtime is not compensated by loop closing capabilities, typical of SLAM. Usually, rovers send surface images to the ground station, and these images are used for multiple purposes, such as scientific and operational planning. The number of images is constrained by the communication bandwidth and power budget. The set of transmitted images can be used as a means to correct the robot’s trajectory in an off-line manner. In this work, a solution is presented to the problem of selecting the optimal set of viewpoints belonging to the planned path from which to capture and transmit images: 1) it guarantees accurate trajectory correction and 2) complies with the maximum number of images that can be transmitted to ground control given the available data budget. To this end, it is proposed: 1) a delocalized/decentralized sensor fusion approach based on pose graph optimization and structure from motion and 2) a strategy to select a minimal set of viewpoints along the trajectory that, given a tentative geometry of the environment and the global path that the rover must follow, minimizes the uncertainty of all the robot poses. Optimal camera viewpoint positions are selected as a function of the planned trajectory, the approximate scene geometry, and the maximum transmittable number of images. The proposed method has been tested on a dataset of stereo-images collected in a representative Martian environment, the ALTEC Mars Terrain Simulator (MTS), with the ExoMars testing rover (ExoTeR—European Space Agency, Paris, France, property). Rover stereo-images ground truth was given with millimetric accuracy by a motion capture (MC) system.

Item URL in elib:https://elib.dlr.de/192898/
Document Type:Article
Title:Viewpoint Selection for Rover Relative Pose Estimation Driven by Minimal Uncertainty Criteria
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Chiodini, SebastianoUniversity of PadovaUNSPECIFIEDUNSPECIFIED
Giubilato, RiccardoUNSPECIFIEDhttps://orcid.org/0000-0002-3161-3171UNSPECIFIED
Pertile, MarcoCISAS - Center for Studies and Activities for Space, University of PadovaUNSPECIFIEDUNSPECIFIED
Franceschetti, PaolaThales Alenia Space ItaliaUNSPECIFIEDUNSPECIFIED
Debei, StefanoDepartment of Mechanical Engineering, University of Padova, Via Venezia 1, 35131 Padova, ItalyUNSPECIFIEDUNSPECIFIED
Date:1 October 2021
Journal or Publication Title:IEEE Transactions on Instrumentation and Measurement
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Active vision, bundle adjustment (BA), structure from motion
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 - Multisensory World Modelling (RM) [RO], R - Planetary Exploration
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Institute of Robotics and Mechatronics (since 2013)
Deposited By: Strobl, Dr. Klaus H.
Deposited On:23 Dec 2022 11:23
Last Modified:23 Dec 2022 11:23

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