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Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden.
Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden..
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2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 2, p. 1699-1706Article in journal (Refereed) Published
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 letter, 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
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 4, no 2, p. 1699-1706
Keywords [en]
Search and rescue robots, motion and path planning, mapping
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-246228DOI: 10.1109/LRA.2019.2897343ISI: 000459538100069Scopus ID: 2-s2.0-85063311333OAI: oai:DiVA.org:kth-246228DiVA, id: diva2:1302396
Note

QC 20190404

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-04-04Bibliographically approved

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Duberg, DanielJensfelt, Patric

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