Sonar-based place recognition using joint sparse coding method

The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a previously visited place. We cast the place recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation offers the key to addressing the problem. In this paper, a joint kernel sparse coding model is developed to tackle the multivariate sonar samples place recognition problem. The experimental results show that the joint sparse coding achieves better performance than 1-Nearest Neighborhood (1-NN) method.