publication . Conference object . 2017

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

Chao Gao; Robert Harle;
Open Access
  • Published: 30 Mar 2017
  • Publisher: IEEE
  • Country: United Kingdom
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 ...
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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