Robots have been widely used for various applications, such as smart transportation, environment monitoring, surveillance, search and rescue. Autonomous navigation, as a core prerequisite for the robots to successfully realise these applications, relies heavily on robot localisation. Global Positioning System (GPS) fails to satisfy many applications in robotics in terms of accuracy and availability. Therefore, robot localisation in GPS-denied 3D space is in great demand. However, due to sensor noise and real world uncertainty, robot localisation in GPS-denied 3D space is a challenging problem. The work in this thesis describes three novel localisation algorithms to localise the robots accurately and efficiently in different .scenarios. .optimal state estimation, including filter based and optimisation based methods, is adopted to elegantly deal with the noise and the uncertainty in a probabilistic perspective. Firstly, a Moving Horizon Estimation (MHE) based localisation algorithm is proposed for single beacon based robot localisation. The performance and observability analyses are also conducted to evaluate the proposed method. Secondly, single beacon based multi-robot cooperative localisation problem is addressed by a constrained MHE based approach. Its discussion answers why and how multi-robot cooperation and optimisation constraints benefit the localisation system. The initial pose estimation problem and observability analysis of the multi-robot system are also studied. Thirdly, an unscented Kalman filter based algorithm is proposed for Vision-aided Inertial Navigation System (VINS) by only using low-cost camera and Inertial Measurement Unit (IMU) to perform pose estimation and camera-IMU extrinsic self-calibration. Trifocal tensor based geometric constraints and point transfer of three-view geometry are incorporated into VINS. Tested by both simulations and experiments, the proposed methods are verified to be effective for robot localisation in GPS-denied 3D space.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:701368 |
| Date | January 2015 |
| Creators | Wang, Sen |
| Publisher | University of Essex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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