A Bayesian Approach for Place Recognition

Ramos, F. T., Upcroft, B., Kumar, S. and Durrant-Whyte, H. F. (2005). A Bayesian Approach for Place Recognition. In: IJCAI-05 Workshop: Reasoning with Uncertainty in Robotics (RUR-05), Edinburgh, Scotland, (1-8). 30 Jul 2005.

Author Ramos, F. T.
Upcroft, B.
Kumar, S.
Durrant-Whyte, H. F.
Title of paper A Bayesian Approach for Place Recognition
Conference name IJCAI-05 Workshop: Reasoning with Uncertainty in Robotics (RUR-05)
Conference location Edinburgh, Scotland
Conference dates 30 Jul 2005
Place of Publication not found
Publisher IJCAI
Publication Year 2005
Sub-type Fully published paper
Start page 1
End page 8
Total pages 8
Language eng
Abstract/Summary 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 classi_cation 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.
Subjects 091405 Mining Engineering
0913 Mechanical Engineering
Keyword Algorithm
mobile robots
Q-Index Code E1

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Created: Tue, 19 Jan 2010, 14:54:57 EST by Thelma Whitbourne on behalf of Faculty Of Engineering, Architecture & Info Tech