Sensor fusion with Gaussian processes

Feng, Shimin (2014) Sensor fusion with Gaussian processes. PhD thesis, University of Glasgow.

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Abstract

This thesis presents a new approach to multi-rate sensor fusion for (1) user matching and (2) position stabilisation and lag reduction. The Microsoft Kinect sensor and the inertial sensors in a mobile device are fused with a Gaussian Process (GP) prior method. We present a Gaussian Process prior model-based framework for multisensor data fusion and explore the use of this model for fusing mobile inertial sensors and an external position sensing device. The Gaussian Process prior model provides a principled mechanism for incorporating the low-sampling-rate position measurements and the high-sampling-rate derivatives in multi-rate sensor fusion, which takes account of the uncertainty of each sensor type. We explore the complementary properties of the Kinect sensor and the built-in inertial sensors in a mobile device and apply the GP framework for sensor fusion in the mobile human-computer interaction area. The Gaussian Process prior model-based sensor fusion is presented as a principled probabilistic approach to dealing with position uncertainty and the lag of the system, which are critical for indoor augmented reality (AR) and other location-aware sensing applications. The sensor fusion helps increase the stability of the position and reduce the lag. This is of great benefit for improving the usability of a human-computer interaction system. We develop two applications using the novel and improved GP prior model. (1) User matching and identification. We apply the GP model to identify individual users, by matching the observed Kinect skeletons with the sensed inertial data from their mobile devices. (2) Position stabilisation and lag reduction in a spatially aware display application for user performance improvement. We conduct a user study. Experimental results show the improved accuracy of target selection, and reduced delay from the sensor fusion system, allowing the users to acquire the target more rapidly, and with fewer errors in comparison with the Kinect filtered system. They also reported improved performance in subjective questions. The two applications can be combined seamlessly in a proxemic interaction system as identification of people and their positions in a room-sized environment plays a key role in proxemic interactions.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Gaussian processes, human-computer interaction, sensor fusion, uncertainty
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Colleges/Schools: College of Science and Engineering > School of Computing Science
Funder's Name: UNSPECIFIED
Supervisor's Name: Murray-Smith, Prof. Roderick and Vinciarelli, Dr. Alessandro
Date of Award: 2014
Depositing User: Mr Shimin Feng
Unique ID: glathesis:2014-5626
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 17 Oct 2014 12:32
Last Modified: 30 Oct 2014 13:48
URI: http://theses.gla.ac.uk/id/eprint/5626

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