“…Magnetic-based positioning: In the past years, there has been a growing interest in localization systems using magnetic fields [3], [10], [34]- [38]. This is partly because cheap magnetic sensors are nowadays available in almost every handheld smart device.…”
Section: Introductionmentioning
confidence: 99%
“…Most existing magnetic localization approaches use either indigenous magnetic fields, such as the Earth's magnetic field and/or the magnetic fields generated by home electronics [36]- [38]. There are also approaches based on low-frequency magnetic fields generated locally for localization purposes [10], [34], [35]. In [10], the field was created by a grid of long coils which is impractical indoors.…”
Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPSdenied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high frequency radio signals. This has led to a plethora of competing solutions targeted towards a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles like walls, floors and people that heavily impact the performance of competing approaches are largely "transparent" to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.
“…Magnetic-based positioning: In the past years, there has been a growing interest in localization systems using magnetic fields [3], [10], [34]- [38]. This is partly because cheap magnetic sensors are nowadays available in almost every handheld smart device.…”
Section: Introductionmentioning
confidence: 99%
“…Most existing magnetic localization approaches use either indigenous magnetic fields, such as the Earth's magnetic field and/or the magnetic fields generated by home electronics [36]- [38]. There are also approaches based on low-frequency magnetic fields generated locally for localization purposes [10], [34], [35]. In [10], the field was created by a grid of long coils which is impractical indoors.…”
Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPSdenied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high frequency radio signals. This has led to a plethora of competing solutions targeted towards a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles like walls, floors and people that heavily impact the performance of competing approaches are largely "transparent" to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.
“…The tracking algorithm is based on an Inertial Measurement Unit (IMU) and a magnetic positioning system, which provides measurement updates to a Kalman Filter. Sheinker et al (2013) propose a 3D positioning system, which is Downloaded by [University of Otago] at 09:04 05 June 2016 based on low-frequency magnetic fields. The system is composed of coils (beacons), which are excited by an AC-source to generate a time-varying magnetic field.…”
“…In the former, magnetic fields are artificially generated in the area of interest using active beacons. Empirical magnetic field models then can be used to predict the observed magnetic strength and the relevant distance values [28]- [30]. Especially, Sheinker et al [30] installed beacons generating low-frequency magnetic field and derived receiver location in a closed form using a theoretical magnetic field model.…”
In robotics, the problem of concurrently addressing the localization and mapping is well defined as simultaneous localization and mapping (SLAM) problem. Since the SLAM procedure is usually recursive, maintaining a certain error bound on the current position estimate is a critical issue. However, when the robot is kidnapped (i.e., the robot is moved by an intentional or unintentional user) or suffers from locomotion failure (due to large slip and falling), the robot will inevitably lose its current position. In this case, immediate recovery of the robot position is essential for seamless operation. In this paper, we present a method of solving both SLAM and relocation problems by employing ambient magnetic and radio measurements. The proposed SLAM is realized in the Rao-Blackwellized particle filter-and grid-based SLAM frameworks, where we exploit the local heading corrections from the magnetic measurements. For the relocation, we design the location signatures using the magnetic and radio measurements, and examine each of the Monte Carlo localization-based and multilayer perceptron-based relocation methods with real-world data. We implement the proposed SLAM and relocation algorithms in an embedded system and verify the feasibility of the proposed methods as an online robot navigation system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.