Self-supervised monocular distance learning on a lightweight micro air vehicle

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)
23 Downloads (Pure)

Abstract

Obstacle detection by monocular vision is challenging because a single camera does not provide a direct measure for absolute distances to objects. A self-supervised learning approach is proposed that combines a camera and a very small short-range proximity sensor to find the relation between the appearance of objects in camera images and their corresponding distances. The method is efficient enough to run real time on a small camera system that can be carried onboard a lightweight MAV of 19 g. The effectiveness of the method is demonstrated by computer simulations and by experiments with the real platform in flight.
Original languageEnglish
Title of host publicationIROS 2016
Subtitle of host publication2016 IEEE/RSJ International Conference
Number of pages6
DOIs
Publication statusPublished - 2016
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of
Duration: 9 Oct 201614 Oct 2016
http://www.iros2016.org/

Conference

Conference2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Abbreviated titleIROS 2016
CountryKorea, Republic of
CityDaejeon
Period9/10/1614/10/16
Internet address

Fingerprint Dive into the research topics of 'Self-supervised monocular distance learning on a lightweight micro air vehicle'. Together they form a unique fingerprint.

  • Cite this