Ground plane detection using an RGB-D camera for quadcopter landing

The purpose of this study is to build an autonomous quadcopter which is capable of automated detection of landing zones and landing. To ensure this there are important steps linked together. The first step is to build a quadcopter that can fly. We have used a commercially available quadcopter platform and an Ardu Pilot Mega control card to build a low cost, easy to implement, and stable platform. An open source firmware, named Arducopter, is used for the control card. This system can take-off, land, hover, and follow a given flight path. In order to detect of landing zones and safe landing, we propose to use an RGB-D camera as a sensor and a small onboard pc as the computing engine. Hence, we have modified the acquired quadcopter frame to integrate additional components. In this thesis, we propose a novel and robust ground plane and obstacle detection algorithm based on depth information using RGB-D camera. Moreover, our method was compared with V-disparity algorithm from the literature. It has been shown that our algorithm performs better than V-disparity method and produces useful ground plane-obstacle segmentations, even for difficult cases. The method is able to work in highly dynamic platforms. This algorithm is generic in the sense that it can be used for different forward-facing RGB-D placements, for example in ground vehicles or robots. Moreover, we developed a pre-process to allow the use of the method for down-facing sensor view positions as the core method is inadequate for landing zone detection. The proposed method compensates the movements of the camera caused by the air vehicle, and detects the ground plane obstacles successfully. It has been shown that the use of RGB-D camera allows ground plane and landing zone detection even in no-light conditions. All necessary components in this thesis were financed by FMV Işık University internal research funds BAP-10B302 project.