Environment selection and hierarchical place recognition

As robots continue to create long-term maps, the amount of information that they need to handle increases over time. In terms of place recognition, this implies that the number of images being considered may increase until exceeding the computational resources of the robot. In this paper we consider a scenario where, given multiple independent large maps, possibly from different cities or locations, a robot must effectively and in real time decide whether it can localize itself in one of those known maps. Since the number of images to be handled by such a system is likely to be extremely large, we find that it is beneficial to decompose the set of images into independent groups or environments. This raises a new question: Given a query image, how do we select the best environment? This paper proposes a similarity criterion that can be used to solve this problem. It is based on the observation that, if each environment is described in terms of its co-occurrent features, similarity between environments can be established by comparing their co-occurrence matrices. We show that this leads to a novel place recognition algorithm that divides the collection of images into environments and arranges them in a hierarchy of inverted indices. By selecting first the relevant environment for the operating robot, we can reduce the number of images to perform the actual loop detection, reducing the execution time while preserving the accuracy. The practicality of this approach is shown through experimental results on several large datasets covering a combined distance of more than 750Km.