Mark Joseph Cummins

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This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach(More)
We describe a new formulation of appearance-only SLAM suitable for very large scale navigation. The system navigates in the space of appearance, assigning each new observation to either a new or previously visited location, without reference to metric position. The system is demonstrated performing reliable online appearance mapping and loop closure(More)
We describe Photo OCR, a system for text extraction from images. Our particular focus is reliable text extraction from smartphone imagery, with the goal of text recognition as a user input modality similar to speech recognition. Commercially available OCR performs poorly on this task. Recent progress in machine learning has substantially improved isolated(More)
This paper describes a probabilistic framework for navigation using only appearance data. By learning a generative model of appearance, we can compute not only the similarity of two observations, but also the probability that they originate from the same location, and hence compute a pdf over observer location. We do not limit ourselves to the kidnapped(More)
This paper describes a probabilistic bail-out condition for multihypothesis testing based on Bennett's inequality. We investigate the use of the test for increasing the speed of an appearance-only SLAM system where locations are recognised on the basis of their sensory appearance. The bail-out condition yields speed increases between 25x-50x on real data,(More)
Loop closure detection systems for monocular SLAM come in three broad categories: (i) map-to-map, (ii) image-to-image and (iii) image-to-map. In this paper, we have chosen an implementation of each and performed experiments allowing the three approaches to be compared. The sequences used include both indoor and outdoor environments and single and multiple(More)
In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full sixdegree-of-freedom outdoor navigation system. It is capable of proThe International Journal of(More)
This paper introduces a probabilistic, two-stage classification framework for the semantic annotation of urban maps as provided by a mobile robot. During the first stage, local scene properties are considered using a probabilistic bagof-words classifier. The second stage incorporates contextual information across a given scene via a Markov Random Field(More)
Large scale exploration of the environment requires a constant time estimation engine. Bundle adjustment or pose relaxation do not fulfil these requirements as the number of parameters to solve grows with the size of the environment. We describe a relative simultaneous localisation and mapping system (RSLAM) for the constant-time estimation of structure and(More)