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Oh, Hyondong
Autonomous Systems Laboratory
Research Interests
  • Autonomy and decision making for unmanned vehicles

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Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments

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Title
Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments
Author
Cho, GangikKim, JongyunOh, Hyondong
Issue Date
2019-06
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
SENSORS, v.19, no.11, pp.2523
Abstract
Due to payload restrictions for micro aerial vehicles (MAVs), vision-based approaches have been widely studied with their light weight characteristics and cost effectiveness. In particular, optical flow-based obstacle avoidance has proven to be one of the most efficient methods in terms of obstacle avoidance capabilities and computational load; however, existing approaches do not consider 3-D complex environments. In addition, most approaches are unable to deal with situations where there are wall-like frontal obstacles. Although some algorithms consider wall-like frontal obstacles, they cause a jitter or unnecessary motion. To address these limitations, this paper proposes a vision-based obstacle avoidance algorithm for MAVs using the optical flow in 3-D textured environments. The image obtained from a monocular camera is first split into two horizontal and vertical half planes. The desired heading direction and climb rate are then determined by comparing the sum of optical flows between half planes horizontally and vertically, respectively, for obstacle avoidance in 3-D environments. Besides, the proposed approach is capable of avoiding wall-like frontal obstacles by considering the divergence of the optical flow at the focus of expansion and navigating to the goal position using a sigmoid weighting function. The performance of the proposed algorithm was validated through numerical simulations and indoor flight experiments in various situations.
URI
https://scholarworks.unist.ac.kr/handle/201301/26829
URL
https://www.mdpi.com/1424-8220/19/11/2523
DOI
10.3390/s19112523
ISSN
1424-8220
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