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A Particle Filter Localisation System for Indoor Track Cycling Using an Intrinsic Coordinate Model

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Peer-reviewed

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Conference Object

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Authors

Godsill, S 

Abstract

© 2018 ISIF In this paper we address the challenging task of tracking a fast-moving bicycle, in the indoor velodrome environment, using inertial sensors and infrequent position measurements. Since the inertial sensors are physically in the intrinsic frame of the bike, we adopt an intrinsic frame dynamic model for the motion, based on curvilinear dynamical models for manoeuvring objects. We show that the combination of inertial measurements with the intrinsic dynamic model leads to linear equations, which may be incorporated effectively into particle filtering schemes. Position measurements are provided through timing measurements on the track from a camera-based system and these are fused with the inertial measurements using a particle filter weighting scheme. The proposed methods are evaluated on synthesised cycling datasets based on real motion trajectories, showing their potential accuracy, and then real data experiments are reported.

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Keywords

46 Information and Computing Sciences, 4007 Control Engineering, Mechatronics and Robotics, 40 Engineering, 4001 Aerospace Engineering

Journal Title

2018 21st International Conference on Information Fusion, FUSION 2018

Conference Name

2018 21st International Conference on Information Fusion (FUSION 2018)

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Publisher

IEEE