A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. 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.

► We develop a Bayesian framework for recognising places from images.
► The approach combines dimensionality reduction with Bayesian learning to produce statistical models of places.
► The models can be learnt from very small sets of images and are resilient to changes in illumination and viewpoint.
► We show consistent experimental results in indoor and outdoor environments.