Perception, Guidance and Navigation for Indoor Autonomous Drone Racing using Deep Learning

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 212
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorJung, Sunggooko
dc.contributor.authorHwang, Sunyouko
dc.contributor.authorShin, Heeminko
dc.contributor.authorShim, David Hyunchulko
dc.date.accessioned2018-05-24T01:32:13Z-
dc.date.available2018-05-24T01:32:13Z-
dc.date.created2018-03-27-
dc.date.created2018-03-27-
dc.date.created2018-03-27-
dc.date.created2018-03-27-
dc.date.issued2018-07-
dc.identifier.citationIEEE ROBOTICS AND AUTOMATION LETTERS, v.3, no.3, pp.2539 - 2544-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10203/242172-
dc.description.abstractIn autonomous drone racing, a drone is required to fly through the gates quickly without any collision. Therefore, it is important to detect the gates reliably using computer vision. However, due to the complications such as varying lighting conditions and gates seen overlapped, traditional image processing algorithms based on color and geometry of the gates tend to fail during the actual racing. In this letter, we introduce a convolutional neural network to estimate the center of a gate robustly. Using the detection results, we apply a line-of-sight guidance algorithm. The proposed algorithm is implemented using low cost, off-the-shelf hardware for validation. All vision processing is performed in real time on the onboard NVIDIA Jetson TX2 embedded computer. In a number of tests our proposed framework successfully exhibited fast and reliable detection and navigation performance in indoor environment.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titlePerception, Guidance and Navigation for Indoor Autonomous Drone Racing using Deep Learning-
dc.typeArticle-
dc.identifier.scopusid2-s2.0-85060566396-
dc.type.rimsART-
dc.citation.volume3-
dc.citation.issue3-
dc.citation.beginningpage2539-
dc.citation.endingpage2544-
dc.citation.publicationnameIEEE ROBOTICS AND AUTOMATION LETTERS-
dc.identifier.doi10.1109/LRA.2018.2808368-
dc.contributor.localauthorShim, David Hyunchul-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAutonomous drone racing-
dc.subject.keywordAuthorsearch and rescue-
dc.subject.keywordAuthordeep learning based object detection-
dc.subject.keywordPlusFRAMEWORK-
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0