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Title: Long term appearance-based mapping with vision and laser
Author: Paul, Rohan
ISNI:       0000 0004 2745 6331
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2012
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This thesis is about appearance-based topological mapping for mobile robots using vision and laser. Our goal is life-long continual operation in outdoor unstruc- tured workspaces. We present a new probabilistic framework for appearance-based mapping and navigation incorporating spatial and visual appearance. Locations are encoded prob- abilistically as random graphs possessing latent distributions over visual features and pair-wise euclidean distances generating observations modeled as 3D constellations of features observed via noisy range and visual detectors. Multi-modal distributions over inter-feature distances are learnt using non-parametric kernel density estima- tion. Inference is accelerated by executing a Delaunay tessellation of the observed graph with minimal loss in performance, scaling log-linearly with scene complexity. Next, we demonstrate how a robot can, through introspection and then targeted data retrieval, improve its own place recognition performance. We introduce the idea of a dynamic sampling set, the onboard workspace representation, that adapts with increasing visual experience of continually operating robot. Based on a topic based probabilistic model of images, we use a measure of perplexity to evaluate how well a working set of background images explains the robot’s online view of the world. O/ine, the robot then searches an external resource to seek additional background images that bolster its ability to localize in its environment when used next. Finally, we present an online and incremental approach allowing an exploring robot to generate apt and compact summaries of its life experience using canon- ical images that capture the essence of the robot’s visual experience-illustrating both what was ordinary and what was extraordinary. Leveraging probabilistic topic models and an incremental graph clustering technique we present an algorithm that scales well with time and variation of experience, generating a summary that evolves incrementally with the novelty of data.
Supervisor: Newman, Paul Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Robotics ; Information engineering ; mobile robotics ; autonomous systems ; machine learning ; computer vision