Efficient Drone Exploration in Real Unknown Environments

Ming Ru Xie, Shing Yun Jung, Kuan Wen Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We propose an autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Recent studies have applied saliency prediction to drone exploration. However, these studies are not sufficiently mature. The ADES system proposes a smaller and faster saliency prediction model and adopts a novel drone exploration approach based on visual-inertial odometry (VIO) to solve the practical problems encountered during exploration, i.e., exploring salient objects without colliding with them and not repeatedly exploring salient objects. The system not only has a performance comparable to that of the state-of-the-art multiple-discontinuous-image saliency prediction network (TA-MSNet) but also enables drones to explore unknown environments more efficiently.

Original languageEnglish
Title of host publicationProceedings - SIGGRAPH Asia 2022 Posters
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450394628
DOIs
StatePublished - 26 Dec 2022
EventSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022 - Daegu, Korea, Republic of
Duration: 6 Dec 20229 Dec 2022

Publication series

NameProceedings - SIGGRAPH Asia 2022 Posters

Conference

ConferenceSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022
Country/TerritoryKorea, Republic of
CityDaegu
Period6/12/229/12/22

Cite this