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Sensors 2012, 12(6), 6764-6801; doi:10.3390/s120606764

Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation

Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz, Poland
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Received: 6 March 2012 / Revised: 19 April 2012 / Accepted: 29 April 2012 / Published: 25 May 2012
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)

Abstract

The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian’s steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS. View Full-Text
Keywords: particle filtering; stereovision; digital maps; GPS; Monte Carlo; dead reckoning particle filtering; stereovision; digital maps; GPS; Monte Carlo; dead reckoning
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Baranski, P.; Strumillo, P. Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation. Sensors 2012, 12, 6764-6801.

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