A Bayesian approach for place recognition

作者：Ramos Fabio^*; Upcroft Ben; Kumar Suresh; Durrant Whyte Hugh

来源：Robotics and Autonomous Systems, 2012, 60(4): 487-497.

DOI：10.1016/j.robot.2011.11.002

• Place recognition
• Bayesian inference
• Dimensionality reduction
• Mobile robots

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.

• 出版日期2012-4