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dc.contributor.author
Bartolomei, Luca
dc.contributor.author
Pinto Teixeira, Lucas
dc.contributor.author
Chli, Margarita
dc.date.accessioned
2021-03-03T08:27:34Z
dc.date.available
2020-09-18T12:27:09Z
dc.date.available
2020-09-18T12:34:34Z
dc.date.available
2021-03-03T08:27:34Z
dc.date.issued
2020
dc.identifier.isbn
978-1-7281-6212-6
en_US
dc.identifier.isbn
978-1-7281-6213-3
en_US
dc.identifier.other
10.1109/IROS45743.2020.9341347
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/441269
dc.identifier.doi
10.3929/ethz-b-000441269
dc.description.abstract
In this work, we present a perception-aware path-planning pipeline for Unmanned Aerial Vehicles (UAVs) for navigation in challenging environments. The objective is to reach a given destination safely and accurately by relying on monocular camera-based state estimators, such as Keyframe-based Visual-Inertial Odometry (VIO) systems. Motivated by the recent advances in semantic segmentation using deep learning, our path-planning architecture takes into consideration the semantic classes of parts of the scene that are perceptually more informative than others. This work proposes a planning strategy capable of avoiding both texture-less regions and problematic areas, such as lakes and oceans, that may cause large drift or failures in the robot’s pose estimation, by using the semantic information to compute the next best action with respect to perception quality. We design a hierarchical planner, composed of an A* path-search step followed by B-Spline trajectory optimization. While the A* steers the UAV towards informative areas, the optimizer keeps the most promising landmarks in the camera’s field of view. We extensively evaluate our approach in a set of photo-realistic simulations, showing a remarkable improvement with respect to the state-of-the-art in active perception.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
ROBOTICS
en_US
dc.subject
Path Planning
en_US
dc.subject
Visual Inertial Odometry
en_US
dc.title
Perception-aware Path Planning for UAVs using Semantic Segmentation
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-02-10
ethz.book.title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
en_US
ethz.pages.start
5808
en_US
ethz.pages.end
5815
en_US
ethz.size
8 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (virtual)
en_US
ethz.event.location
Las Vegas, NV, USA
en_US
ethz.event.date
October 24, 2020 - January 24, 2021
en_US
ethz.notes
Conference lecture held on October 27, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::09559 - Chli, Margarita (ehemalig) / Chli, Margarita (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::09559 - Chli, Margarita (ehemalig) / Chli, Margarita (former)
en_US
ethz.date.deposited
2020-09-18T12:27:20Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-03T08:27:54Z
ethz.rosetta.lastUpdated
2022-03-29T05:33:27Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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