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 Ramos, F.T., Upcroft, B., Kumar, S. & Durrant-Whyte, H.F. |
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A Bayesian Approach for Place Recognition
IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR-05), 2005
Presented at IJCAI Workshop Reasoning with Uncertainty in Robotics, Edinburgh, Scotland, 30 Jul. - 05 Aug. 2005
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Abstract
This paper presents a robust place recognition algorithm for mobile robots. The framework proposed
combines nonlinear dimensionality reduction, nonlinear regression under noise, and variational Bayesian learning to create consistent probabilistic representations of places from images. These generative models are learnt from a few images and used for multi-class place recognition where classifcation is computed from a set of feature-vectors. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions and blurring. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respectively.
This framework has several potential applications such as map building, autonomous navigation,search-rescue tasks and context recognition.
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