First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles

Abstract

Abstract-Obstacle avoidance is desirable for lightweight micro aerial vehicles and is a challenging problem since the payload constraints only permit monocular cameras and obstacles cannot be directly observed. Depth can however be inferred based on various cues in the image. Prior work has examined optical flow, and perspective cues, however these methods cannot handle frontal obstacles well. In this paper we examine the problem of detecting obstacles right in front of the vehicle. We developed a method to detect relative size changes of image patches that is able to detect size changes in the absence of optical flow. The method uses SURF feature matches in combination with template matching to compare relative obstacle sizes with different image spacing. We present results from our algorithm in autonomous flight tests on a small quadrotor. We are able to detect obstacles with a frameto-frame enlargement of 120% with a high confidence and confirmed our algorithm in 20 successful flight experiments. In future work, we will improve the control algorithms to avoid more complicated obstacle configurations.

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

“…We performed an experimental comparison between our autonomous algorithm, the algorithm proposed by [30] and the teleoperation of two persons with different experience levels, using the same MAV. Additionally, we performed an experiment that compares our algorithm in different scenarios.…”

“…There are several applications of SURF, like face detection [26], target tracking [27,28], simple visual navigation [29] and some works with UAVs. One of these is [30], which uses a simple bang-bang control. Our work proposes a real-time obstacle detection algorithm based on feature points and an offline modeling of the MAV for designing a controller for fixed and mobile obstacle avoidance in an unknown controlled environment.…”

Bearing-Based Formation Control of A Group of Agents with Leader-First Follower Structure

“…( (15), we havė [41], [42] and the references therein for examples. Below, we give a sufficient condition for collision-free between agent 3 and its leaders under the dynamics (14).…”

Vision Based Obstacle Detection in Uav Imaging

“…For instance, Mori et al (Mori, 2013) investigate detecting and preventing the obstacles that move toward or in front of MAV camera. Their study assumes that the objects coming toward camera are subject to change in size and dimensions; hence, It uses SURF algorithm features for detecting obstacle position.…”

“…Brain-inspired methods use a similar technique based on how human understands and detects obstacles. Various studies have been performed about braininspired and mono-based techniques (Mori, 2013), (Al-Kaff, 2016), (Zeng, 2016). One of the key features of obstacle detection algorithms is their functionality in complex environments.…”