Magnetic Landmarks Research Articles

The design and application of magnetic landmark used for magnetic fiducialization of HEPS insertion devices*

Abstract Achieving high performance in synchrotron radiation light sources demands increased precision in fiducialization and alignment of insertion devices (IDs). Conventional sensor systems utilizing Hall probes for high-precision magnetic field measurements face limitations in determining the absolute position of the magnetic center of IDs. In this paper, the Magnetic Landmark (MLK), a novel solution based on a magnetized block structure, is designed to address this challenge. The MLK serves as an intermediary, establishing contact between the magnetic center of IDs and their externally accessible fiducials, thus enabling high-precision alignment. Through analysis of magnetic field distribution, self-calibration precision, and practical application of the MLK, the following conclusions are drawn: when the Hall probe is positioned within 0.5 mm of the magnetic field zero point inside the MLK, the measurement accuracy of the zero point for normal and skew quadrupole magnetic fields reaches 7 and 2 μm, respectively. Additionally, the self-measurement accuracy of the MLK is 10 and 4 μm in the transverse and vertical directions, respectively. These accuracies meet our usage requirements. The MLK demonstrates efficacy in aligning the magnetic center of High Energy Photon Source (HEPS) In-air undulators, achieving alignment accuracies of 16 μm in the transverse direction and 11 μm in the vertical direction, surpassing the 30 μm accuracy stipulated in the physical design. Furthermore, the device proves applicable for measuring the magnetic center of other types of HEPS insertion devices, including Cryogenic undulators.

A Survey of Magnetic-Field-Based Indoor Localization

Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed.

MagLand: Magnetic Landmarks for Road Vehicle Localization

Satellite-based systems are the most widespread solution for outdoor localization. However, they present well-known limitations in multipath environments and non-line-of-sight satellite conditions, e.g. tunnels, underground, urban canyons, and multilevel roads, being frequently combined with dead reckoning techniques. Inertial sensors present cumulative errors, and geomagnetic-field information is often distorted by strong local magnetic fields caused by road infrastructure. We turn this magnetic weakness into strength by proposing MagLand, an approach with detection and matching steps to leverage these anomalies as signatures for localization purposes. For anomaly detection, we adopt a window-based technique and apply supervised binary classification, choosing a random forest. We select one nearest centroid algorithm with dynamic time warping to match input data streams to reference signatures, providing guidelines to define and collect them. Real data experiments with off-the-shelf devices in challenging road scenarios show MagLand's feasibility with anomaly detection accuracy of 91% and matching with lane distinction of up to 93%. Magnetic landmarks can be extremely useful to address limitations of current localization systems and improve their performance, e.g. by providing an alternative in GPS limited areas, anchors for integrity monitoring, or resetting dead reckoning cumulative errors.

AMID: Accurate Magnetic Indoor Localization Using Deep Learning.

Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.

Transfer of the magnetic axis of an undulator to mechanical fiducial marks of a laser tracker system

The exact geometric location of the magnetic centers of sensors or sensor systems using Hall probes or pick-up coils is usually not known with high precision. In order to transfer the high spatial accuracy of magnetic measurements to external mechanic fiducials a device called “Magnetic Landmark” was developed and is described in this report. Its purpose is to establish the exact relation between “magnetic” coordinates used on magnetic measurement systems and “mechanic” coordinates used for alignment.The landmark consists of a permanent magnet configuration, which generates a field distribution with well-defined zero crossings in two orthogonal directions, which can be exactly localized with micrometer precision using magnetic measurement systems. For the “mechanic” measurements several redundant monuments for laser fiducials can be used.Using flip tests for the magnetic as well as mechanic measurements the center positions are determined in magnetic and mechanic coordinates. Using them the relation between the magnetic and surveying coordinates can be established with high accuracy.This report concentrates on the description of the landmark. A thorough analysis on achievable accuracy is presented. The method was developed for the alignment of the 91 undulator segments needed for the European XFEL but can be applied to other magnet systems as well.

Development of magnetic navigation method based on distributed control system using magnetic and geometric landmarks

In order for a robot to autonomously run in outdoor environments, a robust and stable navigation method is necessary. Especially, to run in real-world environments, robustness against moving objects is important since many pedestrians and bicycles come and go. Magnetic field, which is not influenced by the moving objects, is considered to be an effective information for autonomous navigation. Localization technique using a magnetic map, which records ambient magnetic field, has been proposed. The magnetic map is expressed as a linear map. When using this linear magnetic map, swerving from the desired path is a fatal problem. It is because that the magnetic map contains only magnetic data on a desired path. In the paper, we propose a novel navigation method which allows a robot to precisely navigate on a desired path even if localization is performed on the basis of the linear magnetic map. The navigation is performed by using a control method based on a DCS (Distributed Control System). In the system, several navigation modules are executed in parallel, and they independently control the robot by using magnetic and geometric landmarks. We conducted three navigation experiments. Our robot could perfectly accomplish all navigation even if it was disturbed by many moving objects during the navigation. The control method based on the DCS could switch the navigation module for controlling the robot to cope against the change of its surroundings. The precise and robust navigation was achieved with the proposed method.

Autonomous Navigation Based on Magnetic and Geometric Landmarks on Environmental Structure in Real World

For the Real World Robot Challenge (RWRC) 2013, a new task was established: every robot was required to search for designated persons. In this paper, therefore, we consider the difficulty of the task and construct a navigation strategy to achieve the task. To navigate a robot on the basis of the strategy, long distance navigation is necessary. We have developed a unique navigation method based on magnetic and geometric landmarks on environmental structures in various locations. This method allows a robot to robustly localize by evaluating the reliability of magnetic and geometric landmarks. By using this method, a robot can navigate stably, even if there are no existing landmarks to serve as objects. We achieved autonomous navigation over long distances and successfully searched out designated persons as the challenge of the RWRC2013. This paper presents our navigation method and discusses long distance navigation using the method.

Localization Using Magnetic Patterns for Autonomous Mobile Robot

In this paper, we present a method of localization using magnetic landmarks. With this method, it is possible to compensate the pose error ( x e, y e, θ e) of a mobile robot correctly and localize its current position on a global coordinate system on the surface of a structured environment with magnetic landmarks. A set of four magnetic bars forms total six different patterns of landmarks and these patterns can be read by the mobile robot with magnetic hall sensors. A sequential motion strategy for a mobile robot is proposed to find the geometric center of magnetic landmarks by reading the nonlinear magnetic field. The mobile robot first moves into the center region of the landmark where it can read the magnetic pattern, after which tracking and global localization can be easily achieved by recognizing the patterns of neighboring landmarks. Experimental results show the effectiveness of the sequential motion strategy for estimating the center of the first encountered landmark as well as the performance of tracking and global localization of the proposed system.

Magnetic landmark-based position correction technique for mobile robots with hall sensors

We propose a precise position error compensation and low-cost relative localization method in structured environments using magnetic landmarks and hall sensors. The proposed methodology can solve the problem of fine localization as well as global localization by tacking landmarks or by utilizing various patterns of magnetic landmark arrangement. In this paper, we consider two patterns of implanted permanent magnets on the surface, namely, at each vertex of regular triangles or rectangles on a flat surface. We show that the rectangular configuration of the permanent magnetic bars is better for a robust localization under sensor noise. For the experiments, permanent magnet sets in rectangular configuration are placed on the floor as landmarks at regular intervals, and magnetic hall sensors are installed at the bottom of a mobile robot. In our implementation, the accuracy after the error compensation is less than 1 mm in the position and less than 1° in the orientation. Due to the low cost and accuracy of the proposed methodology, it would be one of the practical solutions to the pose error correction of a mobile robot in structured environments.

Tessellation of plane with magnetic landmarks for pose control and global localisation

Proposed are regular tessellations of the plane environment where various patterns of magnetic landmarks are arranged in repeated configurations for fine pose tracking control and global localisation of mobile robots. Mobile robots sense the magnetic field with embedded magnetic Hall sensors and can recognise the pattern of the landmark. In the experiments, the accuracy of pose control of a mobile robot with 140 mm diameter is less than 0.5 mm in the position and less than 0.5° in the orientation. Also, the experimental results of landmark recognition demonstrate that the proposed method is suitable for a practical solution to the global localisation in engineered environments.

Magnetic Resonance Imaging-Based Morphometry and Landmark Correlation of Basal Ganglia Nuclei

Magnetic Resonance Imaging-Based Morphometry and Landmark Correlation of Basal Ganglia Nuclei