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|Title:||Multi-robot relative localisation using computer vision||Authors:||Ulun, Soner||Keywords:||Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics||Issue Date:||2019||Source:||Ulun, S. (2019). Multi-robot relative localisation using computer vision. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||This thesis focusses on robots with a marsupial relationship that use computer vision as their main sensor. This work is focused on two separate projects. In computer vision, drift is still a major drawback when using vision-based localisation methods. The first project of this thesis combines the position information from two Unmanned Ground Vehicles (UGV) to reduce this drift in GPS denied environments. Relative pose measurements between two robots are used in a similar way to loop-closure. The robots start at a nearby location and share a common field of view. The sharing of the information is done periodically. The pose of each robot is calculated using visual odometry. The relative position of the robots is calculated using both monocular and stereo image pairs, for initial estimation and refining respectively. A specific algorithm is proposed to combine the visual odometry and relative pose estimations from one robot to another in a pose graph optimisation scheme to reduce the drift. Experiments have been conducted on both simulations and real-life. The second project introduces two new methods that enabled the autonomous take-off, tracking and precise landing with an Unmanned Aerial Vehicle (UAV) on a UGV using a dual monocular camera setup. These methods used predefined markers to compute the relative position of the UAV from the landing platform that is attached to the UGV. The cameras on the UAV have different focal lengths, which are providing better performance than using a single camera. Different markers and detectors pairs are analysed and compared to achieve a reliable pose estimation. For a UAV, the most critical parts of the flight are take-off and landing, as this is the time most prone to crashes. Two methods are proposed, named as'Landing calibration' and "Marker Connector", that helped the UAV to land precisely at the desired position in a GPS denied environment. Each method has been verified with more than hundred real-world experiments.||URI:||https://hdl.handle.net/10356/83293
|Fulltext Permission:||embargo_20221101||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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