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MSGD: Scalable Back-end for Indoor Magnetic Field-based GraphSLAM

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Gao, C 
Harle, RK 

Abstract

Simultaneous Localisation and Mapping (SLAM) systems that recover the trajectory of a robot or mobile device are characterised by a front-end and back-end. The front-end uses sensor observations to identify loop closures; the back-end optimises the estimated trajectory to be consistent with these closures. The GraphSLAM framework formulates the back-end problem as a graph-based optimisation on a pose graph.

This paper describes a back-end system optimised for very dense sequence-based loop closures. This arises when the front-end generates magnetic loop closures, among other things. Magnetic measurements are fast varying, which is good for localisation, but the requirement for high sampling rates (50 Hz+) produces many more loop closures than conventional systems. To date, however, there is no study optimising GraphSLAM back-end for sequence-based magnetic loop closures. Hence we introduce a novel variant of the Stochastic Gradient Descent-based SLAM algorithm called MSGD (Magnetic-SGD). We use high-accuracy groundtruth system and extensive real datasets to evaluate MSGD against state-of-the-art back-end algorithms. We demonstrate MSGD is at least as good as the best competitor algorithm in terms of quality, while being faster and more scalable.

Description

Keywords

simultaneous localization and mapping, trajectory, algorithm design and analysis, estimation, optimization

Journal Title

2017 IEEE International Conference on Robotics and Automation (ICRA)

Conference Name

ICRA 2017

Journal ISSN

1050-4729

Volume Title

Publisher

IEEE