Abstract
Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity.
Highlights
IntroductionIndoor environments are the main scenarios of people’s activity, and it is estimated that people spend about 87 percent of their time indoors [2]
In ubiquitous and mobile computing, location is the most fundamental element [1].Indoor environments are the main scenarios of people’s activity, and it is estimated that people spend about 87 percent of their time indoors [2]
Map filtering uses Bayesian techniques to reduce the uncertainty of indoor location estimations that violate the spatial constraints, thereby enhancing the localization accuracy
Summary
Indoor environments are the main scenarios of people’s activity, and it is estimated that people spend about 87 percent of their time indoors [2]. While location-based context-aware systems, such as navigation and goods recommendation, are extending from outdoor spaces to indoor spaces [3], inferring the accurate locations of people in indoor spaces is still challenging. One of the commonly-used indoor localization methods is Pedestrian Dead Reckoning (PDR) [4], which has become a mainstream method due to the advent of sensor-rich smartphones. Map filtering uses Bayesian techniques to reduce the uncertainty of indoor location estimations that violate the spatial constraints (e.g., walking through walls or obstacles), thereby enhancing the localization accuracy. Map filtering takes into account a limited number of spatial contexts, and the definition of violating the spatial constraints is imprecise. In the map filtering approach, wall-crossing estimations are identified if the line segment between two successive estimations intersects with a wall, Sensors 2016, 16, 2137; doi:10.3390/s16122137 www.mdpi.com/journal/sensors
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