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dc.contributor.author
Müller, Hanna
dc.contributor.author
Zimmerman, Nicky
dc.contributor.author
Polonelli, Tommaso
dc.contributor.author
Magno, Michele
dc.contributor.author
Behley, Jens
dc.contributor.author
Stachniss, Cyrill
dc.contributor.author
Benini, Luca
dc.date.accessioned
2023-09-06T07:20:47Z
dc.date.available
2023-08-19T03:59:36Z
dc.date.available
2023-09-06T07:20:47Z
dc.date.issued
2023
dc.identifier.isbn
979-8-3503-9624-9
en_US
dc.identifier.other
10.23919/DATE56975.2023.10137327
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/627324
dc.description.abstract
Nano-size unmanned aerial vehicles (UAVs) hold enormous potential to perform autonomous operations in complex environments, such as inspection, monitoring or data collection. Moreover, their small size allows safe operation close to humans and agile flight. An important part of autonomous flight is localization, a computationally intensive task, especially on a nano-UAV that usually has strong constraints in sensing, processing and memory. This work presents a real-time localization approach with low-element-count multizone range sensors for resource-constrained nano-UAVs. The proposed approach is based on a novel miniature 64-zone time-of-flight sensor from STMicroelectronics and a RISC-V-based parallel ultra-low-power processor to enable accurate and low latency Monte Carlo Localization on-board. Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31.2 m(2) map with 0.15 m accuracy and an above 95% success rate. The achieved accuracy is sufficient for localization in common indoor environments. We analyze trade-offs in using full and half-precision floating point numbers as well as a quantized map and evaluate the accuracy and memory footprint across the design space. Experimental evaluation shows that parallelizing the execution for 8 RISC-V cores brings a 7x speedup and allows us to execute the algorithm onboard in real-time with a latency of 0.2-30 ms (depending on the number of particles), while only increasing the overall drone power consumption by 3-7%. Finally,
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
UAV
en_US
dc.subject
Localization
en_US
dc.subject
Autonomous navigation
en_US
dc.subject
nano-UAVs
en_US
dc.subject
Perception
en_US
dc.subject
ToF Array
en_US
dc.title
Fully On-board Low-Power Localization with Multizone Time-of-Flight Sensors on Nano-UAVs
en_US
dc.type
Conference Paper
dc.date.published
2023-06-02
ethz.book.title
2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
en_US
ethz.pages.start
10137327
en_US
ethz.size
6 p.
en_US
ethz.event
26th Design, Automation and Test in Europe Conference and Exhibition (DATE 2023)
en_US
ethz.event.location
Antwerp, Belgium
en_US
ethz.event.date
April 17-19, 2023
en_US
ethz.identifier.wos
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::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
ethz.date.deposited
2023-08-19T03:59:49Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-09-06T07:20:48Z
ethz.rosetta.lastUpdated
2024-02-03T03:18:54Z
ethz.rosetta.versionExported
true
ethz.COinS
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