Avoiding collision with moving obstacles for UAVs using robust ego-motion estimation technique

Jae-Hung Han; Jeonghwan Park; Andrew Jaeyong Choi

doi:10.1117/12.2583377

22 March 2021 Avoiding collision with moving obstacles for UAVs using robust ego-motion estimation technique

Jae-Hung Han, Jeonghwan Park, Andrew Jaeyong Choi

Proceedings Volume 11588, Active and Passive Smart Structures and Integrated Systems XV; 115881A (2021) https://doi.org/10.1117/12.2583377
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Abstract

This paper proposes a vision-based collision avoidance system for unmanned aerial vehicles (UAVs). A method to detect and avoid approaching objects is necessary for UAVs since they are inherently vulnerable to external impacts. To resolve common issues with motion detection on a moving platform, computer vision algorithms such as optical flow and homography transform are utilized. The robustness of these algorithms is improved by employing characteristics of differential images. The proposed method is implemented in a camera-equipped onboard computer and then mounted onto a UAV as a collision avoidance system. It performs evasive maneuvers to avoid various objects thrown in its flight path, demonstrating its functionality and robustness.

Conference Presentation

Citation Download Citation

Jae-Hung Han, Jeonghwan Park, and Andrew Jaeyong Choi "Avoiding collision with moving obstacles for UAVs using robust ego-motion estimation technique", Proc. SPIE 11588, Active and Passive Smart Structures and Integrated Systems XV, 115881A (22 March 2021); https://doi.org/10.1117/12.2583377

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available