Combinatorial maps for simultaneous localization and map building (SLAM)

Abstract

In this article, we focus on environment models for the well-known simultaneous localisation and map building (SLAM) problem, which has received considerable attention in the robotics community over the past few years. First, we compare different existing map representations to discuss their advantages and limitations in the scope of indoor robotics applications. Then, we define a highly structured map model which combines different kinds of representations, including space-based, grid-based as well as feature-based formats. This model also provides topological information such as adjacency links, which are similar to the topological layer used in geographical information systems. We explain how to build and update maps according to this model, using a mobile robot equipped with a laser scanner and underline how the structure of our representation may increase robustness in a Kalman-based SLAM process. Finally, we show some preliminary experiments and propose a few perspectives for this work.