Control of Low Speed Flight in Cluttered Environments Using Image Motion and Ranging on a Micro-Air Vehicle

Abstract

This thesis focuses on the issue of real-time low speed flight of Micro-Air-Vehicles (MAVs) in cluttered environments. In order to achieve real-time autonomous operation, we have developed a navigation system for a Micro-Air-Vehicle using a RGB-D camera. Low-cost range sensors are perfect alternatives for expensive laser scanners in many application areas. For our experiments, we make use of a recent development in consumer-grade range sensing technology, the Microsoft Kinect sensor, consisting of an infrared laser emitter, an infrared camera and a RGB camera. Dominant plane estimation is a fundamental task for obstacle detection and autonomous navigation of MAVs. A modified region growing technique for plane detection is developed with an incremental approach, optimised plane and mean square error calculations, resulting in improvements to accuracy and efficiency. The Image Interpolation Algorithm (I2A) is used to calculate optical flow from the RGB-D camera intensity image sequences and corresponding depth information. A novel ego-motion recovery method is proposed by combining the range data with the optical flow field Image Jacobian. For our algorithm, assumptions about the moving object or dynamic scenes are not needed. An Extended Kalman Filter is used to fuse inertial data with the ego-motion parameters. By integrating the egomotion, estimation of the velocity and position of the quadrotor is obtained in three dimensional space. With lack of information about the surroundings, path planning in a cluttered environment is a very challenging task and fast re-planning is required when new obstacles are detected. The enhanced D* Lite algorithm is proposed for shortening the travel distance in cluttered environments and a novel dense scene flow estimation method is presented which can be used for detecting dynamic obstacles. An AscTec pelican quadrotor is used for real-time experiments. A Vicon Motion Tracking System provides the measurement parameters for ground truth in order to analyse the system error. Based on real-time experiments implemented in cluttered environments, the different parts of the research results are tested and validated.