Object Detection by Monocular Camera and Laser Sensors for Small Unmanned Aerial Vehicle

Peter Dzurovčin,Milan Džunda,Jozef Galanda,Libor Švadlenka,Iveta Vajdová

doi:10.35116/aa.2019.0013

Peter Dzurovčin, Milan Džunda + Show 3 more

Open Access

https://doi.org/10.35116/aa.2019.0013

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Abstract

In this paper, we present selected options for detection and avoidance of obstacles by small unmanned vehicles. The solution to this problem is very complicated mainly because UAVs have a limited load capacity as well as energy sources. Sensors that can be used to solve this task must meet the minimum weight and power requirements. We decided to use a stereo camera and a laser because of the requirements that we set up earlier. The size of the obstacle is determined by the SURF algorithm and the Harris detector.

Highlights

One possibility of creating an anti-collision system for Unmanned Aerial Vehicle (UAV) is to use the relative navigation system
Obstacle detection and avoidance system highly depend on sensors that being employed on the UAVs, type of the most research and considered popular choice for the system will be a Vision based and Laser based
Experiments are performed by moving the UAV from reference image frame (RIF) position to target image frame (TIF) position and record the image frame captured

Summary

Introduction

One possibility of creating an anti-collision system for Unmanned Aerial Vehicle (UAV) is to use the relative navigation system. The disadvantage of this solution is that such a system can only work in a communication network. In accordance with [24], we present selected options for detection and avoidance of obstacles by small UAVs. An Unmanned Aerial Vehicle (UAV) as the name implies, it is an aircraft without a pilot on board or attached to it. An Unmanned Aerial Vehicle (UAV) as the name implies, it is an aircraft without a pilot on board or attached to it It can fly autonomously by pre-programmed the UAV’s system or controlled manually by the pilot on the ground. Vision based method has rich information regarding bearing angle of the detected obstacles, while distance from the vehicle to obstacles are poorly recognized [24]

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Conclusion