publication . Conference object . 2017

MSGD: Scalable back-end for indoor magnetic field-based GraphSLAM

Name: MSGD: Scalable back-end for indoor magnetic field-based GraphSLAM
Keywords: algorithm design and analysis, estimation, optimization

Chao Gao; Robert Harle;

Open Access

Published: 30 Mar 2017

Publisher: IEEE

Country: United Kingdom

Summary

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 ...

doi: 10.1109/icra.2017.7989444 , 10.17863/cam.8686

Subjects

free text keywords: algorithm design and analysis, estimation, optimization, Algorithm design, Computer vision, Mobile device, Robot, Sampling (statistics), Simultaneous localization and mapping, Trajectory, Artificial intelligence, business.industry, business, Stochastic gradient descent, Scalability, Algorithm, Mathematics

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