Hierarchical SLAM: real-time accurate mapping of large environments

• Published in: IEEE transactions on robotics 2005-08, Vol.21 (4), p.588-596
• Main Authors: Estrada, C., Neira, J., Tardos, J.D.
• Format: Article
• Language: English
• Subjects: Analytical models; Applied sciences; Computational complexity; Computational efficiency; Computational modeling; Computer science; control theory; systems; Control theory. Systems; Convergence; Exact sciences and technology; Information filtering; Information filters; Large maps; local maps; loop closing; Mapping; Robotics; Robots; Simultaneous localization and mapping; stochastic mapping; Stochastic models; Stochastic processes
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2005.844673

In this paper, we present a hierarchical mapping method that allows us to obtain accurate metric maps of large environments in real time. The lower (or local) map level is composed of a set of local maps that are guaranteed to be statistically independent. The upper (or global) level is an adjacency graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained at this level in a relative stochastic map. We propose a close to optimal loop closing method that, while maintaining independence at the local level, imposes consistency at the global level at a computational cost that is linear with the size of the loop. Experimental results demonstrate the efficiency and precision of the proposed method by mapping the Ada Byron building at our campus. We also analyze, using simulations, the precision and convergence of our method for larger loops.