An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression

An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a...

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Bibliographic Details
Main Authors: Helmons, R. (Author), Kok, M. (Author), Viset, F. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Computational complexity
Computationally efficient
Extended Kalman filters
Filter-based
Functions
Gaussian distribution
Gaussian noise (electronic)
Gaussian process regression
Indoor positioning systems
Kalman filtering
Kalman-filtering
Localisation
localization
magnetic field
Magnetic fields
Magnetic-field
Mapping
Monte Carlo methods
Rao-blackwellized particle filter
Reduced rank gaussian process
Robotics
simultaneous localization and mapping
Simultaneous localization and mapping
Time step
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