Mobility Increases Localizability

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.

Section: Discussionmentioning

confidence: 99%

GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors

Shang

Wen

et al. 2016

“…Conventional PDR methods consist of three phases: (i) step counting, (ii) step length estimation, and (iii) walking direction estimation [20]. The number of steps is determined by the threshold, peak detection, or zero-crossing method in a periodic change of the accelerometer signal [21,22].…”

Section: Related Workmentioning

confidence: 99%

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Kang

Lee

Eom

2018

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

“…Like indoor maps, landmarks, which can be detected by the unique patterns of smartphone sensor data, can be used to correct the PDR trajectory [10]. Indoor landmarks detected by HAR provide a new opportunity for indoor localization [15].…”

Section: Related Workmentioning

confidence: 99%

“…In many cases, semantic information is as valuable as the location. For example, from a human cognition perspective, in comparing the position coordinates, it is more valuable to know if a location is a room, a corridor or stairs [10]. Furthermore, it is also more convenient for a user to obtain semantic information (e.g., “turn left”, “turn right”, “go upstairs”, “go downstairs” and “go into a room”) than information about a route.…”

Section: Introductionmentioning

confidence: 99%

Activity Recognition and Semantic Description for Indoor Mobile Localization

Guo

Xiong

Zheng

et al. 2017

As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications.