# Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps

arXiv: https://arxiv.org/abs/1804.01926 · HTML: https://mlsanity.com/paper/1804.01926/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy](https://mlsanity.com/paper/2206.06435/) (2022)
- [Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions](https://mlsanity.com/paper/2210.09168/) (2022)
- [Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field](https://mlsanity.com/paper/2203.15866/) (2022)
- [Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks](https://mlsanity.com/paper/2409.07652/) (2024)
- [Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes](https://mlsanity.com/paper/2203.13110/) (2022)
- [Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression](https://mlsanity.com/paper/2310.16574/) (2023)
- [Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression](https://mlsanity.com/paper/2310.16577/) (2023)
- [Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices](https://mlsanity.com/paper/2408.02346/) (2024)
- [Adaptive Basis Function Selection for Computationally Efficient Predictions](https://mlsanity.com/paper/2408.07480/) (2024)
- [Physically-informed change-point kernels for structural dynamics](https://mlsanity.com/paper/2506.11625/) (2025)

## Abstract (arXiv)

We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao-Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic field SLAM algorithm in terms of both computational complexity and map storage.