Place recognition based on Latent Dirichlet Allocation

This paper describes a new scheme based on Latent Dirichlet Allocation for place recognition of mobile robot system. It firstly extracts the local features from the training images, forms a discrete set of “image words” which are commonly known as vocabulary or codebook, and each image is represented as a frequency vector based on this vocabulary. Then the model based on Latent Dirichlet Allocation is used to learn themes distribution in the training set and testing images. Finally the unknown test images are recognized according to the similarity of themes distribution. In order to evaluate the method, we perform it on the IDOL2 Database and our own pictures. Experimental results show that the method has good robustness to different types of variations, including different illumination conditions, different perspective and other changes over long periods in real-world environments.