| Summary: | The ability to estimate egomotion is at the heart of safe and reliable mobile autonomy. By inferring pose changes from sequential sensor measurements, egomotion estimation forms the basis of mapping and navigation pipelines, and permits mobile robots to self-localize within environments where external localization information may be intermittent or unavailable. Visual egomotion estimation, also known as visual odometry, has become ubiquitous in mobile robotics due to the availability of high-quality, compact, and inexpensive cameras that capture rich representations of the world. Classical visual odometry pipelines make simplifying assumptions that, while permitting reliable operation in ideal conditions, often lead to systematic error. In this dissertation, we present four ways in which conventional pipelines can be improved through the addition of a learned hyper-parametric model. By combining traditional pipelines with learning, we retain the performance of conventional techniques in nominal conditions while leveraging modern high-capacity data-driven models to improve uncertainty quantification, correct for systematic bias, and improve robustness to deleterious effects by extracting latent information in existing visual data. We demonstrate the improvements derived from our approach on data collected in sundry settings such as urban roads, indoor labs, and planetary analogue sites in the Canadian High Arctic. Ph.D.
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