Robust Sensing and Control of Micro Aerial Vehicles using a Monocular Camera and an Inertial Measurement Unit

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Copyright: Li, Ping
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Abstract
This thesis is concerned with the motion estimation and control of Micro Aerial Vehicles (MAVs) using a monocular camera and Inertial Measurement Unit (IMU). The primary contribution is the development of robust visual algorithms dealing with real-world challenges like illumination variation and low-texture scenes, and the design of simple sensor fusion techniques for reliable and high-frequency motion estimation and even structure estimation. Real-time performance is achieved and the algorithms have been validated using real sensory data and tested for on-board closed-loop control of MAVs. Hover, talk-off and landing of a low-cost quadrotor are first considered. Image loom is used for determining the rough height during take-off and for providing initial height value for the biologically-inspired 3D snapshot algorithm. The problem of illumination change is addressed by developing a fast and robust template matching algorithm. External disturbances can temporarily push the drone away from the visual anchor point. This condition is detected using a confidence measure and it is shown how by integrating frame-to-frame motion the vehicle can be guided back to achieve loop closure. The approach has been tested extensively in flight tests both indoors and outdoors. The estimation of the visual scale factor is then considered by fusing visual estimation with inertial data. The robustness of a popular OF algorithm is improved using a transformed binary image from the intensity image. A new homography model is developed in which it is proposed to directly obtain the speed up to an unknown scale factor from the homography matrix. For sensor fusion, Kalman filters are initially applied separately to the three axes for state estimation. It is then proposed to use the whole homography matrix as the system measurement to further compute the surface normal. It is also shown that only part of the homography matrix is needed for metric state estimation. Real images and IMU data recorded from our quadrotor platform show the superiority of the proposed method over the traditional approach that decomposes the homography matrix for both state estimation and slope estimation. This thesis also touches upon the online estimation of camera intrinsic parameters.
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Author(s)
Li, Ping
Supervisor(s)
Garratt, Matthew
Lambert, Andrew
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Publication Year
2016
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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