Localization of autonomous mobile robots (AMRs) in enclosed or semi-enclosed environments such as offices, hotels, hospitals, indoor parking facilities, and underground spaces where GPS signals are weak or unavailable remains a major obstacle to the deployment of fully autonomous systems. Infrastructure-based localization approaches, such as QR codes and RFID, are constrained by high installation and maintenance costs as well as limited flexibility, while onboard sensor-based methods, including LiDAR- and vision-based solutions, are affected by ambiguous geometric features and frequent occlusions caused by dynamic obstacles such as pedestrians. Ambient magnetic field (AMF)-based localization has therefore attracted growing interest in recent years because it does not rely on external infrastructure or geometric features, making it well-suited for AMR applications such as service robots and security robots. However, magnetometer measurements are often corrupted by distortions caused by ferromagnetic materials present on the sensor platform, which bias the AMF and degrade localization reliability. As a result, accurate magnetometer calibration to estimate distortion parameters becomes essential. Conventional calibration methods that rely on rotating the magnetometer are impractical for large and heavy platforms. To address this limitation, this paper proposes a robust simultaneous localization and calibration (RoSLAC) approach based on alternating optimization, which iteratively and efficiently estimates both the platform pose and magnetometer calibration parameters. Extensive evaluations conducted in high-fidelity simulation and real-world environments demonstrate that the proposed RoSLAC method achieves high localization accuracy while maintaining low computational cost compared with state-of-the-art magnetometer calibration techniques.
Figures & tables
Fig. 1: The measured values of a magnetic sensor can be influenced by two main factors: (a) interior instrumental imperfection of the magnetometer, and (b) the presence of exterior ferromagnetic materials in the platform.
Fig. 2: Flowchart of the proposed method. The overall pipeline can be decomposed into two sub-modules: (a) ambient magnetic map pre-building, and (b) the online RoSLAC procedure, featuring sequence accumulation and alternating optimization.
Fig. 3: Five different experimental environments. Environments (1)–(4) correspond to real-world scenarios, while (5) represents a simulated warehouse environment constructed in the Gazebo simulator.
Fig. 4: The Scout Mini AMR is employed in both real-world and simulated environments to collect experimental data.
Localization ATE (m)
Scenario
RS
RP
SO
PF
raw
p-cal
raw
p-cal
raw
p-cal
raw
p-cal
Office
0.114
0.114
0.103
0.107
0.167
0.130
0.153
0.121
Carpark
0.103
0.104
0.127
0.132
0.414
0.161
0.308
0.146
Hotel
0.117
0.118
0.208
0.195
0.448
0.133
0.163
0.112
Platform
0.093
0.083
0.100
0.112
0.886
0.199
0.260
0.194
TABLE I: The localization accuracy comparison in Absolute Trajectory Error (ATE, meter). The best result is highlighted in BOLD
Fig. 5: Visualization of the convergence of the magnetometer calibration parameters, measured as the ℓ2−norm deviation from the reference values, under different scenarios.
Fig. 6: Localization stability in different scenarios. Performance is compared with LiDAR-based relocalization [ 38 ] . Errors smaller than 0.3m are marked in blue (well estimated), errors between 0.3m and 1m are marked in orange (poorly estimated), and errors larger than 1m are marked in red (failed).
Calibration Parameters
Error
Scenario
No.
c11
c22
c33
b1
b2
b3
c11
c22
c33
b1
b2
b3
ℓ2−norm
ATE
RS
RP
RS
RP
RS
RP
RS
RP
RS
RP
RS
RP
RS
RP
RS
RP
Office
1
1.01
0.98
0.99
19.49
20.60
20.17
0.02
0.03
0.01
0.05
0.03
0.02
0.64
3.47
0.49
0.83
0.45
3.14
1.06
4.82
0.11
0.17
2
1.03
0.94
0.96
5.33
5.39
5.36
0.02
0.01
0.01
0.02
0.03
0.03
0.30
0.54
0.17
0.46
0.55
0.89
0.73
1.21
0.11
0.10
Carpark
3
0.97
0.97
0.97
5.23
5.82
6.04
0.01
0.01
0.01
0.04
0.01
0.04
0.45
0.87
0.40
1.03
0.18
0.84
0.68
1.64
0.10
0.20
4
0.98
0.98
1.00
10.85
9.87
12.68
0.01
0.07
0.02
0.02
0.01
0.03
0.67
4.68
0.37
1.88
0.20
1.19
0.85
5.29
0.11
0.52
TABLE III: Comparison of element-wise calibration parameter accuracy (in μT ) under different initial guesses, along with the corresponding localization accuracy measured by Absolute Trajectory Error (ATE, in meters).
Fig. 7: Effect of the sequence accumulation length on the mean per-frame processing time of RoSLAC and the corresponding localization accuracy. The mean processing time is additionally compared with that of RBPF as a reference.
Algorithm Block
RoSLAC
S1
S2
S3
Sequence Accumulation
✓
✓
Online Calibration
✓
✓
Online Localization
✓
✓
✓
✓
TABLE IV: Different combinations of the modules proposed in RoSLAC .
Localization in challenging environments, such as GNSS-denied, geometrically repetitive, or textureless scenes commonly found in offices, hotels, and underground parking facilities, remains an open problem for reliable autonomous mobile robot (AMR) deployment. Single-modality localization methods are inherently limited by the constraints of individual sensors. Although multimodal fusion frameworks have shown improved robustness, most existing approaches still rely heavily on geometric or texture features, or on infrastructure-based beacons, which increase installation and maintenance costs while reducing deployment flexibility. Recently, ambient magnetic field (AMF)-based localization has attracted growing attention because it does not depend on geometric or texture features, nor does it require additional infrastructure, making it a promising complementary modality for AMR localization. However, existing studies have only explored such fusion in pedestrian scenarios using smartphone-mounted sensor suites, and practical solutions for AMR systems remain largely unexplored. To address this gap, this article proposes a magnetometer-inertial-LiDAR fused multimodal localization framework with a custom-designed sensor suite, termed MIL-LC, which provides reliable localization even when LiDAR suffers from geometric degeneration or when the magnetic map changes during long-term deployment. Extensive experiments in both simulation and real-world environments demonstrate that the proposed MIL-LC framework achieves robust and accurate localization performance.
Qiyang Lyu, Zhenyu Wu, Wei Wang +2
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798
Indoor localisation techniques suffer from attenuated Global Navigation Satellite System (GNSS) signals and from the accumulation of unbounded drift by integration of proprioceptive sensors. Magnetic field-based Simultaneous Localisation and Mapping (SLAM) reduces drift through loop closures by revisiting previously seen locations, but extended exploration of unseen areas remains challenging. Recently, magnetometer arrays have demonstrated significant benefits over single magnetometers, as they can directly estimate the odometry. However, inconsistencies between magnetometer measurements negatively affect odometry estimates and complicate loop closure detection. We propose two filtering algorithms: The first focuses on magnetic field-based SLAM using a magnetometer array (SLAMma). The second extends this to jointly estimate the magnetometer calibration parameters (SLCAMma). We demonstrate, using Monte Carlo simulations, that the calibration parameters can be accurately estimated when there is sufficient orientation excitation, and that magnetometers achieve inter-sensor measurement consistency regardless of the type of motion. Experimental validation on ten datasets confirms these results, and we demonstrate that in cases where single magnetometer SLAM fails, SLAMma and SLCAMma provide good trajectory estimates with, more than 80% drift reduction compared to integration of proprioceptive sensors.
Thomas Edridge, Manon Kok
1DCSC, Delft University of Technology, the Netherlands.
Ultra-Wideband (UWB) ranging has become a viable option for industrial Autonomous Mobile Robot (AMR) localization due to improved accuracy and low cost. However, real-world deployments remain limited by two recurring challenges: calibrating static anchors can be time-consuming and error-prone, and integrating UWB with existing onboard sensors requires careful design to ensure robust and consistent pose estimation. Addressing these challenges, this paper presents an end-to-end pipeline that combines automatic anchor calibration with a generic multi-sensor estimator tailored to surface-bound vehicle motion. It targets existing AMR stacks in scenarios where robot pose priors are available for initialization. The calibration stage estimates anchor positions and range biases, while the localization stage fuses UWB with proprioceptive sensing in a bias-aware Extended Kalman Filter to improve consistency without extensive parameter tuning. Experiments on a commercial logistics AMR in a warehouse setting demonstrate accurate positioning indoors and across outdoor transitions, with improved consistency compared to an earlier estimator formulation. Evaluation on an independent forklift dataset further indicates transferability to other platforms. The method remains effective in test cases with limited line-of-sight and sparse anchor coverage. These results show that UWB localization can be deployed with substantially reduced manual effort while preserving the accuracy required for industrial AMRs. The collected warehouse dataset is made publicly available.
Alexander Raab, Giulio Delama, Roland Jung +1
AGILOX Services GmbH, Austria · Control of Networked Systems Group, University of Klagenfurt, Austria