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Efficient Autonomous Exploration Planning of Large Scale 3D-Environments
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. Royal Institute of Technology, Stockholm Sweden.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8546-4431
Royal Institute of Technology, Stockholm Sweden.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (KPLAB)
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2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045Article in journal, Letter (Refereed) Epub ahead of print
Abstract [en]

Exploration is an important aspect of robotics, whether it is for mapping, rescue missions or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this work we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019.
Keywords [en]
Search and Rescue Robots, Motion and Path Planning, Mapping
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-154335DOI: 10.1109/LRA.2019.2897343OAI: oai:DiVA.org:liu-154335DiVA, id: diva2:1285787
Projects
FACT (SSF)WASP
Funder
Swedish Foundation for Strategic Research Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-02-05 Created: 2019-02-05 Last updated: 2019-03-06Bibliographically approved

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Tiger, MattiasHeintz, Fredrik

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