Abstract
There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.
Highlights
The development of reliable indoor positioning based on Micro-Electro-Mechanical Sensors (MEMS) has been a central aim of technology researchers in recent years
Wireless radio-based communications have the intrinsic problem of signal unreliability, due to multipath effects and No Line-of-Sight signal conditions, in indoor environments [4], which can be improved by using the abovementioned sensors
The remainder of the paper is structured as follows: the related work and challenges are described section three presents the study dataset, in section four, two main positioning techniques which can work independently—that is, only smartphone sensors and a simulation for Infrastructure-based implementation (e.g., 5G)—are discussed; section five presents the autonomous position estimation method, where particle filtering map-matching is discussed; in section six, we present an initial fusion concept for the autonomous approach and 5G—which can be realized with and without taking mapmatching into consideration; and section seven provides our conclusions and directions for future works
Summary
The development of reliable indoor positioning based on Micro-Electro-Mechanical Sensors (MEMS) has been a central aim of technology researchers in recent years. Every smartphone— carried around by almost everyone today—can be counted as a unique source of such an application [2] Another possibility would be 5G New Radio (NR) networks, which are expected to enable highly accurate positioning as an ideal support for sensor-based autonomous approaches. The observations from different sensors, such as vision features [6,7], point clouds [8,9] from cameras, and Laser Identification Detection and Ranging (LIDAR) sensors, can be combined with odometry information to correct for drift and to provide efficient localization and tracking at the same time [10,11,12] Such vision-based auxiliary features are not always available. The remainder of the paper is structured as follows: the related work and challenges are described section three presents the study dataset, in section four, two main positioning techniques which can work independently—that is, only smartphone sensors and a simulation for Infrastructure-based implementation (e.g., 5G)—are discussed; section five presents the autonomous position estimation method, where particle filtering map-matching is discussed; in section six, we present an initial fusion concept for the autonomous approach and 5G—which can be realized with and without taking mapmatching into consideration; and section seven provides our conclusions and directions for future works
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