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• W2205569118 abstract "We consider the problem of indoor human trajectory identification using odometry data from smartphone sensors. Given a segmented trajectory, a simplified map of the environment, and a set of error thresholds, we implement a map-matching algorithm in a urban setting and analyze the accuracy of the resulting path. We also discuss aggregation of user step data into a segmented trajectory. Besides providing an interesting application of learning human motion in a constrained environment, we examine how the uncertainty of the snapped trajectory varies with path length. We demonstrate that as new segments are added to a path, the number of possibilities for earlier segments is monotonically non-increasing. Applications of this work in an urban setting are discussed, as well as future plans to develop a formal theory of odometry-based map-matching." @default.
• W2205569118 created "2016-06-24" @default.
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• W2205569118 date "2015-09-01" @default.
• W2205569118 modified "2023-09-25" @default.
• W2205569118 title "Indoor trajectory identification: Snapping with uncertainty" @default.
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• W2205569118 doi "https://doi.org/10.1109/iros.2015.7354066" @default.
• W2205569118 hasPublicationYear "2015" @default.
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