Whereas outdoor navigation systems typically rely upon GPS, indoor systems have to rely upon different techniques for localizing the user, as GPS signals cannot be received indoors. Over the past decade various indoor navigation systems have been developed. This paper provides a comprehensive overview of existing indoor navigation systems and analyzes the different techniques used for: (1) locating the user; (2) planning a path; (3) representing the environment; and (4) interacting with the user. Our survey identifies a number of research issues that could facilitate large scale deployment of indoor navigation systems.
Indoor localization and navigation systems for individuals with Visual Impairments (VIs) typically rely upon extensive augmentation of the physical space, significant computational resources, or heavy and expensive sensors; thus, few systems have been implemented on a large scale. This work describes a system able to guide people with VIs through indoor environments using inexpensive sensors, such as accelerometers and compasses, which are available in portable devices like smart phones. The method takes advantage of feedback from the human user, who confirms the presence of landmarks, something that users with VIs already do when navigating in a building. The system calculates the user's location in real time and uses it to provide audio instructions on how to reach the desired destination. Initial early experiments suggested that the accuracy of the localization depends on the type of directions and the availability of an appropriate transition model for the user. A critical parameter for the transition model is the user's step length. Consequently, this work also investigates different schemes for automatically computing the user's step length and reducing the dependence of the approach on the definition of an accurate transition model. In this way, the direction provision method is able to use the localization estimate and adapt to failed executions of paths by the users. Experiments are presented that evaluate the accuracy of the overall integrated system, which is executed online on a smart phone. Both people with VIs and blindfolded sighted people participated in the experiments, which included paths along multiple floors that required the use of stairs and elevators.
The accuracy of pedometry varies depending on where an inertial sensor is located on the body. Motivated by the increasing popularity of wearable computing, this paper investigates the accuracy with which pedometry can be achieved on a head-mounted device: something previous research has not investigated. A study with 16 subjects compares the accuracy of pedometry for walking and running with an inertial sensor located at the head, pocket and hand/arm. Our study did not detect a significant difference in step counting accuracy between sensor locations, which demonstrates the feasibility of pedometry-based apps for head-mounted displays.
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