Real-time stereo vision for collision detection on autonomous UAVs

Aleksandar Stanoev, Nicolas Audinet, Scott Tancock, Naim Dahnoun

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
358 Downloads (Pure)

Abstract

Collision detection is an important unsolved problem in the domain of modern UAV, which would enable safe navigation in unknown environments. Stereo vision provides a compact, lightweight and low-power solution. This paper describes an adaptive system for achieving real-time stereo vision for collision detection on an embedded GPU. Several optimisations are described including using sensor fusion with an ultrasonic sensor to better filter noise, organising the computations to take advantage of the platform's heterogeneous architecture and using GPU textures to benefit from caching. A discussion of the hardware features is provided, followed by the algorithm and implementation details for disparity calculations and finally a method for identifying objects from a disparity map. The system was implemented on an NVIDIA Tegra X1, achieving 48 FPS on a 320×240 image.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Imaging Systems and Techniques (IST), 2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781538616208
ISBN (Print)9781538616215
DOIs
Publication statusPublished - 18 Jan 2018
EventIEEE International Conference on Imaging Systems & Techniques - Beijing, China
Duration: 18 Oct 201720 Oct 2017

Conference

ConferenceIEEE International Conference on Imaging Systems & Techniques
CountryChina
CityBeijing
Period18/10/1720/10/17

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  • Cite this

    Stanoev, A., Audinet, N., Tancock, S., & Dahnoun, N. (2018). Real-time stereo vision for collision detection on autonomous UAVs. In Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST), 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IST.2017.8261524