MEMS-based inertial navigation based on a magnetic field map

Kok, Manon; Wahlström, Niklas; Schön, Thomas B.; Gustafsson, Fredrik

doi:10.1109/icassp.2013.6638911

Cited by 15 publications

(16 citation statements)

References 12 publications

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“…Although the idea has been around for a while (see e.g. [10], [11] for early results), only recently it has attracted the interest of many research groups [5], [12], [13], [14], [15]. In our earlier work we have shown that an indoor magnetic field contains enough information (Fig.…”

Section: Introductionmentioning

confidence: 98%

“…Often in estimation one has to rely heavily on predictive models, such as Gaussian Processes (GP) [6], to for example interpolate and extrapolate map values in unmeasured areas [4], [5]. If the available data is scarce or the models inaccurate, the estimates may contain considerable estimation errors, especially when extrapolating.…”

Section: Introductionmentioning

confidence: 99%

“…Since their introduction [1], particle filters (PF) have become a widely used tool for solving non-linear non-Gaussian estimation problems, occurring in fields such as machine vision, tracking, and robotic localization and mapping [2], [3], [4], [5]. Often in estimation one has to rely heavily on predictive models, such as Gaussian Processes (GP) [6], to for example interpolate and extrapolate map values in unmeasured areas [4], [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monty Hall Particle filter: A new method to tackle predictive model uncertainties

Vallivaara

Kemppainen

Poikselkä

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

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This paper proposes a simple adaptive weight computing method for particle filters that utilizes knowledge about predictive model uncertainty. In each time step the particles are assigned into subsets based on the corresponding uncertainty estimates. The weights are then updated based on accumulated subset-inclusion and likelihood information using a discrete set of measurement likelihood functions. By controlling the aggressiveness of the weight computing, the method strives to achieve faster convergence without losing robustness to model errors. Two localization experiments are conducted to verify that the method has a clear advantage over particle filters with single likelihood function. In the first experiment we use synthetic Gaussian Process data. In the second experiment real indoor magnetic field data with very coarse interpolation and uncertainty approximation is used to verify the method's effectiveness in real-world scenarios. One of the main advantages of the proposed method is that despite its flexibility, it adds only little implementational or computational overhead to conventional particle filters.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monty Hall Particle filter: A new method to tackle predictive model uncertainties

Vallivaara

Kemppainen

Poikselkä

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

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“…Mechanic tracking systems as described above can hence not be used due to their limited region of operation. Also, magnetic tracking such as [75] is excluded because of the same reason.…”

Section: Pose Estimation In Room-scaled Environmentsmentioning

confidence: 99%

On Pose Estimation in Room-Scaled Environments

Nyqvist¹

2016

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Systra mi, du är bäst! Ikväll ska vi tokdansa med Ronny och Ragge på bordet tills byxorna spricker så att mamma får dåndimpen och pappa ramlar ur soffan. AbstractPose (position and orientation) tracking in room-scaled environments is an enabling technique for many applications. Today, virtual reality (vr) and augmented reality (ar) are two examples of such applications, receiving high interest both from the public and the research community. Accurate pose tracking of the vr or ar equipment, often a camera or a headset, or of different body parts is crucial to trick the human brain and make the virtual experience realistic. Pose tracking in room-scaled environments is also needed for reference tracking and metrology. This thesis focuses on an application to metrology. In this application, photometric models of a photo studio are needed to perform realistic scene reconstruction and image synthesis. Pose tracking of a dedicated sensor enables creation of these photometric models. The demands on the tracking system used in this application is high. It must be able to provide sub-centimeter and sub-degree accuracy and at same time be easy to move and install in new photo studios.The focus of this thesis is to investigate and develop methods for a pose tracking system that satisfies the requirements of the intended metrology application. The Bayesian filtering framework is suggested because of its firm theoretical foundation in informatics and because it enables straightforward fusion of measurements from several sensors. Sensor fusion is in this thesis seen as a way to exploit complementary characteristics of different sensors to increase tracking accuracy and robustness. Four different types of measurements are considered; inertialmeasurements, images from a camera, range (time-of-flight) measurements from ultra wide band (uwb) radio signals, and range and velocity measurements from echoes of transmitted acoustic signals.A simulation study and a study of the Cramér-Rao lower filtering bound (crlb) show that an inertial-camera system has the potential to reach the required tracking accuracy. It is however assumed that known fiducial markers, that can be detected and recognized in images, are deployed in the environment. The study shows that many markers are required. This makes the solution more of a stationary solution and the mobility requirement is not fulfilled. A simultaneous localization and mapping (slam) solution, where naturally occurring features are used instead of known markers, are suggested solve this problem. Evaluation using real data shows that the provided inertial-camera slam filter suffers from drift but that support from uwb range measurements eliminates this drift. The slam solution is then only dependent on knowing the position of very few stationary uwb transmitters compared to a large number of known fiducial markers. As a last step, to increase the accuracy of the slam filter, it is investigated if and how range measurements can be complemented with velocity measurement obtained as a resu...

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“…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]. However, fusion of the accelerometer and magnetometers for vehicle trajectory estimation under the above mentioned circumstances is a novel approach.…”

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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MEMS-based inertial navigation based on a magnetic field map

Cited by 15 publications

References 12 publications

Monty Hall Particle filter: A new method to tackle predictive model uncertainties

Monty Hall Particle filter: A new method to tackle predictive model uncertainties

On Pose Estimation in Room-Scaled Environments

Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

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