In order for autonomous vehicles to achieve life-long operation in outdoor environments, navigation systems must be able to cope with visual change---whether it's short term, such as variable lighting or weather conditions, or long term, such as different seasons. As a GPS is not always reliable, autonomous vehicles must be self sufficient with onboard sensors. This thesis examines the problem of localisation against a known map across extreme lighting and weather conditions using only a stereo camera as the primary sensor. The method presented departs from traditional techniques that blindly apply out-of-the-box interest-point detectors to all images of all places. This naive approach fails to take into account any prior knowledge that exists about the environment in which the robot is operating. Furthermore, the point-feature approach often fails when there are dramatic appearance changes, as associating low-level features such as corners or edges is extremely difficult and sometimes not possible. By leveraging knowledge of prior appearance, this thesis presents an unsupervised method for learning a set of distinctive and stable (i.e., stable under appearance changes) feature detectors that are unique to a specific place in the environment. In other words, we learn place-dependent feature detectors that enable vastly superior performance in terms of robustness in exchange for a reduced, but tolerable metric precision. By folding in a method for masking distracting objects in dynamic environments and examining a simple model for external illuminates, such as the sun, this thesis presents a robust localisation system that is able to achieve metric estimates from night-today or summer-to-winter conditions. Results are presented from various locations in the UK, including the Begbroke Science Park, Woodstock, Oxford, and central London.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635217 |
| Date | January 2014 |
| Creators | McManus, Colin |
| Contributors | Newman, Paul |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:088715bb-cfba-408e-b1f6-b307ae43be97 |
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