Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
On the Induction of Topological Maps from Sequences of Colour Histograms
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Fast automatic compensation of under/over-exposured image regions
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Bayesian inference in the space of topological maps
IEEE Transactions on Robotics
Map-based navigation in mobile robots
Cognitive Systems Research
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In this paper we propose a method for vision only topological simultaneous localisation and mapping (SLAM). Our approach does not use motion or odometric information but a sequence of noisy visual measurements observed by traversing an environment. In particular, we address the perceptual aliasing problem which occurs using external observations only in topological navigation. We propose a Bayesian inference method to incrementally build a topological map by inferring spatial relations from the sequence of observations while simultaneously estimating the robot's location. The algorithm aims to build a small map which is consistent with local adjacency information extracted from the sequence measurements. Local adjacency information is incorporated to disambiguate places which otherwise would appear to be the same. Experiments in an indoor environment show that the proposed technique is capable of dealing with perceptual aliasing using visual observations only and successfully performs topological SLAM.