Robot localization with sparse scan-based maps

Abstract

Occupancy grid maps are a popular method for representing the environment in the context of robot navigation tasks. However, occupancy grid maps can have a high memory demand that grows quadratically with the range of the sensor. In this paper, we introduce a memory-efficient map representation that is based on a constant set of individual scans. To make these scan-based maps suitable for autonomous robot navigation, we propose probabilistically sound methods for both mapping and localization. To solve the mapping problem, our approach incrementally selects scans based on the additional information they provide relative to the scans previously selected. Using these selected scans, we perform an Monte Carlo Localization (MCL) approach with a sensor model optimized for the scan-based representation of our map. We present extensive experiments in which we evaluate our approach using real world data recorded in a garage parking scenario with an autonomous car as well as a robot localization problem in an indoor environment. The results demonstrate that our approach can cope with high sensor noise and that it achieves comparable localization accuracy while at the same time consuming only a fraction of memory compared to regular occupancy grid maps.