Abstract
One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.
Highlights
During the last decade, with the developments in microelectronics and the increase of computing efficiency, the use of Unmanned Aerial Vehicles (UAVs) is no longer restricted to the military purposes only
In this paper, a bio-inspired approach using a monocular camera is presented to mimic the human behavior of obstacle detection and avoidance applied on UAVs
The experiments have been performed with a Parrot AR.Drone 2.0 quadcopter [36]. This control system is governed by the inputs, the implemented controller realize the UAV actual position, orientation and velocity
Summary
With the developments in microelectronics and the increase of computing efficiency, the use of Unmanned Aerial Vehicles (UAVs) is no longer restricted to the military purposes only. In addition to its lightweight and the low power consumption, the cameras provide rich information of the environment They are considered as important sensors mounted on the small and micro UAVs. In vision-based navigation systems, different approaches were presented to solve the problem of obstacle detection and avoidance. Gibson illustrated the ability of the human visual system to identify the approaching of the objects related to the expansion of its size, by both eyes or even one eye [26] From this aspect, in this paper, a bio-inspired approach using a monocular camera is presented to mimic the human behavior of obstacle detection and avoidance applied on UAVs. The system is divided into two main stages: Vision-Based Navigation and Guidance in which, the obstacle detection algorithm is performed based on the input images captured from the front camera.
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