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A Bayesian approach for place recognition
Authors:Fabio Ramos  Ben Upcroft  Suresh Kumar  Hugh Durrant-Whyte
Affiliation:1. Bob Shapell School of Social Work, Tel Aviv University, Ramat Aviv 69978, Israel;2. Human Services Department, The Max Stern Yezreel Valley College
Abstract: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.
Keywords:
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