A review of optimisation strategies used in simultaneous localisation and mapping

ABSTRACT

This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping (SLAM) problem. The focus is on the optimisation-based SLAM back end. The strategies are classified based on their purposes such as reducing the computational complexity, improving the convergence and improving the robustness. It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution. The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end. Although more research is needed to further improve the SLAM back end, nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.

KEYWORDS:

• SLAM
• optimisation
• sparseness
• dimension reduction
• submap joining

Disclosure statement

No potential conflict of interest was reported by the author.

ORCID

Shoudong Huang  http://orcid.org/0000-0002-6124-4178

Additional information

Notes on contributors

Shoudong Huang

Shoudong Huang received the Bachelor and Master degrees in Mathematics, Ph.D. in Automatic Control from Northeastern University, People's Republic of China, in 1987, 1990, and 1998, respectively. He is currently an Associate Professor at Centre for Autonomous Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests include nonlinear control systems and mobile robots simultaneous localisation and mapping (SLAM), exploration and navigation.