The gist of maps - summarizing experience for lifelong localization

Abstract

Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robots over long time scales, we must develop algorithms that get better with repeated visits to the same environment, while still working within a fixed computational budget. This paper presents and evaluates an algorithm that alternates between online place recognition and offline map maintenance with the goal of producing the best performance with a fixed map size. At the core of the algorithm is the concept of a Summary Map, a reduced map representation that includes only the landmarks that are deemed most useful for place recognition. To assign landmarks to the map, we use a scoring function that ranks the utility of each landmark and a sampling policy that selects the landmarks for each place. The Summary Map can then be used by any descriptor-based inference method for constant-complexity online place recognition. We evaluate a number of scoring functions and sampling policies and show that it is possible to build and maintain maps of a constant size and that place-recognition performance improves over multiple visits.