Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion

Frassl, Martin; Angermann, Michael; Lichtenstern, Michael; Robertson, Patrick; Julian, Brian J.; Doniec, Marek

doi:10.1109/iros.2013.6696459

Cited by 94 publications

(85 citation statements)

References 31 publications

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“…Frassl et al achieved precise localization based on the 2D magnetic map [18]. However, constructing the 2D magnetic map in wide environments is difficult, namely adopting this technique to outdoor navigation is also difficult.…”

Section: Autonomous Navigation Methodsmentioning

confidence: 99%

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

et al. 2014

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Background: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. Methods: 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. Results and discussion: We conducted three navigation experiments. Our robot could perfectly accomplish all navigation even if it was disturbed by many moving objects during the navigation. Conclusions: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.

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Section: Autonomous Navigation Methodsmentioning

confidence: 99%

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

et al. 2014

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“…The measurement function h a (x (m) t|T ; θ) is defined in (12) and it is apparent that (40) does not exhibit a closed form solution for any of κ, η, or l. Hence, numerical methods have to be employed in this case. To this end, the Newton-Raphson method is used in this paper.…”

Section: ) Accelerometermentioning

confidence: 99%

“…Similarly, magnetometers have been used for tracking in different contexts such as underwater object tracking or road vehicle tracking [7]- [10]. Magnetometers, in combination with odometry, have also been shown to be a viable option for indoor simultaneous localization and mapping (SLAM) for robots [11], [12] as well as humans [12], [13]. Finally, location tracking by using magnetometers and MEMS inertial measurements based on a priori known maps was proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

2015

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Abstract-This article shows how a particle smoother based system identification method can be applied for estimating the trajectory of road vehicles. As sensors, a combination of an accelerometer measuring the road surface vibrations and a magnetometer measuring magnetic disturbances mounted on the side of the road are considered. First, sensor models describing the measurements of the two sensors are introduced. It is shown that these depend on unknown, static parameters that have to be considered in the estimation. Second, the sensor models are combined with a two-dimensional constant velocity motion model. Third, the system identification algorithm is introduced which iteratively runs a Rao-Blackwellized particle smoother to estimate the vehicle trajectory followed by an expectation-maximization step to estimate the parameters. Finally, the method is applied to both simulation and measurement data. It is found that the method works well in general and some issues when real data is considered are identified as future work.

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“…Angermann et al (2012);Frassl et al (2013). This approach assumes that knowledge of the magnetic field is represented as a map in which we want to localize the sensor.…”

Section: Magnetometersmentioning

confidence: 99%

Probabilistic modeling for positioning applications using inertial sensors

Kok¹

2014

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In this thesis, we consider the problem of estimating position and orientation (6D pose) using inertial sensors (accelerometers and gyroscopes). Inertial sensors provide information about the change in position and orientation at high sampling rates. However, they suffer from integration drift and hence need to be supplemented with additional sensors. To combine information from the inertial sensors with information from other sensors we use probabilistic models, both for sensor fusion and for sensor calibration.Inertial sensors can be supplemented with magnetometers, which are typically used to provide heading information. This relies on the assumption that the measured magnetic field is equal to a constant local magnetic field and that the magnetometer is properly calibrated. However, the presence of metallic objects in the vicinity of the sensor will make the first assumption invalid. If the metallic object is rigidly attached to the sensor, the magnetometer can be calibrated for the presence of this magnetic disturbance. Afterwards, the measurements can be used for heading estimation as if the disturbance was not present. We present a practical magnetometer calibration algorithm that is experimentally shown to lead to improved heading estimates. An alternative approach is to exploit the presence of magnetic disturbances in indoor environments by using them as a source of position information. We show that in the vicinity of a magnetic coil it is possible to obtain accurate position estimates using inertial sensors, magnetometers and knowledge of the magnetic field induced by the coil.We also consider the problem of estimating a human body's 6D pose. For this, multiple inertial sensors are placed on the body. Information from the inertial sensors is combined using a biomechanical model which represents the human body as consisting of connected body segments. We solve this problem using an optimization-based approach and show that accurate 6D pose estimates are obtained. These estimates accurately represent the relative position and orientation of the human body, i.e. the shape of the body is accurately represented but the absolute position can not be determined.To estimate absolute position of the body, we consider the problem of indoor positioning using time of arrival measurements from an ultra-wideband (uwb) system in combination with inertial measurements. Our algorithm uses a tightlycoupled sensor fusion approach and is shown to lead to accurate position and orientation estimates. To be able to obtain position information from the uwb measurements, it is imperative that accurate estimates of the receivers' positions and clock offsets are known. Hence, we also present an easy-to-use algorithm to calibrate the uwb system. It is based on a maximum likelihood formulation and represents the uwb measurements assuming a heavy-tailed asymmetric noise distribution to account for measurement outliers.v Populärvetenskaplig sammanfattning I denna licentiatsavhandling betraktar vi problemet att skatta positio...

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Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion

Cited by 94 publications

References 31 publications

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

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

Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

Probabilistic modeling for positioning applications using inertial sensors

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