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• W2060252158 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." @default.
• W2060252158 created "2016-06-24" @default.
• W2060252158 creator A5051095136 @default.
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• W2060252158 date "2012-05-25" @default.
• W2060252158 modified "2023-10-13" @default.
• W2060252158 title "Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation" @default.
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• W2060252158 doi "https://doi.org/10.3390/s120606764" @default.
• W2060252158 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3435951" @default.
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