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Robust Visual-Inertial State Estimation with Multiple Odometries and Efficient Mapping on an MAV with Ultra-Wide FOV Stereo Vision

Müller, Marcus Gerhard and Steidle, Florian and Schuster, Martin and Lutz, Philipp and Maier, Moritz and Stoneman, Samantha and Tomic, Teodor and Stürzl, Wolfgang (2018) Robust Visual-Inertial State Estimation with Multiple Odometries and Efficient Mapping on an MAV with Ultra-Wide FOV Stereo Vision. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. IROS 2018, 2018-10-01 - 2018-10-05, Madrid, Spain. doi: 10.1109/iros.2018.8594117. ISBN 978-153868094-0. ISSN 2153-0858.

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Abstract

The here presented flying system uses two pairs of wide-angle stereo cameras and maps a large area of interest in a short amount of time. We present a multicopter system equipped with two pairs of wide-angle stereo cameras and an inertial measurement unit (IMU) for robust visual-inertial navigation and time-efficient omni-directional 3D mapping. The four cameras cover a 240 degree stereo field of view (FOV) vertically, which makes the system also suitable for cramped and confined environments like caves. In our approach, we synthesize eight virtual pinhole cameras from four wide-angle cameras. Each of the resulting four synthesized pinhole stereo systems provides input to an independent visual odometry (VO). Subsequently, the four individual motion estimates are fused with data from an IMU, based on their consistency with the state estimation. We describe the configuration and image processing of the vision system as well as the sensor fusion and mapping pipeline on board the MAV. We demonstrate the robustness of our multi-VO approach for visual-inertial navigation and present results of a 3D-mapping experiment.

Item URL in elib:https://elib.dlr.de/122805/
Document Type:Conference or Workshop Item (Speech)
Additional Information:Best paper award.
Title:Robust Visual-Inertial State Estimation with Multiple Odometries and Efficient Mapping on an MAV with Ultra-Wide FOV Stereo Vision
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Müller, Marcus GerhardUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Steidle, FlorianUNSPECIFIEDhttps://orcid.org/0000-0001-6935-9810UNSPECIFIED
Schuster, MartinUNSPECIFIEDhttps://orcid.org/0000-0002-6983-3719UNSPECIFIED
Lutz, PhilippUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Maier, MoritzUNSPECIFIEDhttps://orcid.org/0000-0002-3447-7611UNSPECIFIED
Stoneman, SamanthaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Tomic, TeodorUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Stürzl, WolfgangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2018
Journal or Publication Title:2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1109/iros.2018.8594117
ISSN:2153-0858
ISBN:978-153868094-0
Status:Published
Keywords:MAV; UAV; visual odometry; multiple cameras; robust state estimation; 3D mapping; robust visual-inertial navigation; ultra-wide FOV stereo vision; robotic
Event Title:IROS 2018
Event Location:Madrid, Spain
Event Type:international Conference
Event Start Date:1 October 2018
Event End Date:5 October 2018
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):Vorhaben Intelligente Mobilität (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Müller, Marcus Gerhard
Deposited On:07 Dec 2018 17:24
Last Modified:24 Apr 2024 20:26

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