2017
DOI: 10.3390/ijgi6070221 View full text |Buy / Rent full text
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Abstract: Abstract:In recent years, using smartphones to determine pedestrian locations in indoor environments is an extensively promising technique for improving context-aware applications. However, the applicability and accuracy of the conventional approaches are still limited due to infrastructure-dependence, and there is seldom consideration of the semantic information inherently existing in maps. In this paper, a semantically-constrained low-complexity sensor fusion approach is proposed for the estimation of the us… Show more

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“…Map matching methods are applied with PDR to better estimate locations [26,27]. Zhou et al (2017) alleviated the accumulated error of PDR by correcting locations with a navigation network [26].…”
Section: Pdr-related Methodsmentioning
“…Map matching methods are applied with PDR to better estimate locations [26,27]. Zhou et al (2017) alleviated the accumulated error of PDR by correcting locations with a navigation network [26]. With the help of particle filter on the network, the positioning accuracy of PDR can be raised up to 1.23 m. In addition, PDR, human activity recognition (HAR), and indoor landmarks are included to better estimate user trajectories [28].…”
Section: Pdr-related Methodsmentioning
“…The error accumulation caused by low-cost sensors makes the PDR method alone unable to achieve acceptable accuracy [31]. In addition to the research on heading estimation, some researchers focused on indoor trajectory tracking and correction techniques [5,18,19,[31][32][33]. Guo et al [32,34] constructed a semantic-rich indoor link-node model and utilized the inferred semantic information to match with this model to derive the correct user trajectory.…”
Section: Introductionmentioning
“…Guo et al [32,34] constructed a semantic-rich indoor link-node model and utilized the inferred semantic information to match with this model to derive the correct user trajectory. Zhou et al [31] utilized a semantic augmented route network graph with an adaptive edge length to provide semantic constraint for the trajectory calibration using a particle filter algorithm, which obtained an enhanced accuracy of 1.23 m, with the indoor semantic information attached to each pedestrian's motion. Wang et al [5] presented a correlation matching algorithm based on map projection and the zone division of a floor map to constrain the accumulation of errors associated with the PDR positioning, which eliminated the accumulation error of PDR systems to a certain extent and improved the quality and accuracy of the positioning results.…”
Section: Introductionmentioning
“…A considerable amount of research on indoor navigation has been published, but the subject is treated separately in different research fields, ranging from engineering (indoor positioning) (e.g., [6][7][8][9][10][11][12][13][14][15]), informatics (indoor modeling) (e.g., [16][17][18][19]), and architectural design (e.g., [20][21][22][23]), to psychology (analysis of spatial-related perceptual and cognitive processes) (e.g., [24][25][26][27][28]). Much of the current literature on navigation systems pays particular attention to the localization or other components of the system, while there has been little discussion about the optimization of route calculations.…”
Section: Introductionmentioning