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A High-Performance Tracking System based on Camera and IMU
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2013 (English)In: 16th International Conference on Information Fusion (FUSION), 2013, IEEE , 2013, 2065-2072 p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider an indoor tracking system consisting of an inertial measurement unit (IMU) and a camera that detects markers in the environment. There are many camera based tracking systems described in literature and available commercially, and a few of them also has support from IMU. These are based on the best-effort principle, where the performance varies depending on the situation. In contrast to this, we start with a specification of the system performance, and the design isbased on an information theoretic approach, where specific user scenarios are defined. Precise models for the camera and IMU are derived for a fusion filter, and the theoretical Cramér-Rao lower bound and the Kalman filter performance are evaluated. In this study, we focus on examining the camera quality versus the marker density needed to get at least a one mm and one degree accuracy in tracking performance.

Place, publisher, year, edition, pages
IEEE , 2013. 2065-2072 p.
Keyword [en]
Tracking, IMU, Vision, Indoor, Landmarks, Cameras, Lenses, Earth, Accuracy, Optical sensors, Noise, Optical imaging
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-96751ISI: 000341370000274ISBN: 9786058631113 (print)ISBN: 9781479902842 (print)ISBN: 9786058631113 (print)OAI: oai:DiVA.org:liu-96751DiVA: diva2:643227
Conference
2013 16th International Conference on Information Fusion, Istanbul, Turkey, July 9-12, 2013
Projects
VPS
Funder
Swedish Foundation for Strategic Research
Available from: 2013-08-26 Created: 2013-08-26 Last updated: 2016-11-22Bibliographically approved
In thesis
1. On Pose Estimation in Room-Scaled Environments
Open this publication in new window or tab >>On Pose Estimation in Room-Scaled Environments
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Pose (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 result of the Doppler effect. Especially, focus is put on analyzing the correlation between the range and velocity measurements and the implications this correlation has for filtering. The investigation is done in a theoretical study of reflected known signals (compare with radar and sonar) where the crlb is used as an analyzing tool. The theory is validated on real data from acoustic echoes in an indoor environment.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. 76 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1765
National Category
Control Engineering Computer Vision and Robotics (Autonomous Systems) Computer Systems
Identifiers
urn:nbn:se:liu:diva-132735 (URN)10.3384/lic.diva-132735 (DOI)9789176856284 (ISBN)
Presentation
2016-12-09, Visionen, B-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2016-11-22 Created: 2016-11-22 Last updated: 2016-11-29Bibliographically approved

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Nyqvist, HannaGustafsson, Fredrik

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