A review of optimisation strategies used in simultaneous localisation and mapping

Publication Type:
Journal Article
Citation:
Journal of Control and Decision, 2019, 6 (1), pp. 61 - 74
Issue Date:
2019-01-02
Filename Description Size
JCD_v2.pdfAccepted Manuscript Version1.1 MB
Adobe PDF
Full metadata record
© 2018, © 2018 Northeastern University, China. 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.
Please use this identifier to cite or link to this item: