Reasoning with Uncertainty in Robotics ( RUR - 05 ) Edinburgh , Scotland , 30 July 2005

This paper presents a robust place recognition algorithm for mobile robots. The framework pro-posed combines nonlinear dimensionality reduction, nonlinear regression under noise, and variationalBayesian learning to create consistent probabilistic representations of places from images. These gener-ative models are learnt from a few images and used for multi-class place recognition where classificationis computed from a set of feature-vectors. Recognition can be performed in near real-time and accountsfor complexity such as changes in illumination, occlusions and blurring. The algorithm was tested witha mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respec-tively. This framework has several potential applications such as map building, autonomous navigation,search-rescue tasks and context recognition.