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Pose Estimation Using Monocular Vision and Inertial Sensors Aided with Ultra Wide Band
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2015 (English)In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015, IEEE , 2015Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work.

Place, publisher, year, edition, pages
IEEE , 2015.
Keyword [en]
inertial sensor (IMU), ultra wide band (UWB), monocular camera, simultaneous localization and mapping (SLAM)
National Category
Signal Processing Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-122140DOI: 10.1109/IPIN.2015.7346940ISI: 000379160900049ISBN: 9781467384025 (print)ISBN: 9781467384018 (electronic)OAI: oai:DiVA.org:liu-122140DiVA: diva2:862105
Conference
Sixth International Conference on Indoor Positioning and Indoor Navigation, Banff, October 13-16, 2015
Projects
Virtual Photo Studio (VPS)
Funder
Swedish Foundation for Strategic Research , IIS11-0081Swedish Research CouncilSecurity Link
Available from: 2015-10-20 Created: 2015-10-20 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, Hanna E.Skoglund, Martin A.Hendeby, GustafGustafsson, Fredrik

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