Abstract
Abstract. The ubiquity of mobile devices (such as smartphones and tablet-PCs) has encouraged the use of location-based services (LBS) that are relevant to the current location and context of a mobile user. The main challenge of LBS is to find a pervasive and accurate personal navigation system (PNS) in different situations of a mobile user. In this paper, we propose a method of personal navigation for pedestrians that allows a user to freely move in outdoor environments. This system aims at detection of the context information which is useful for improving personal navigation. The context information for a PNS consists of user activity modes (e.g. walking, stationary, driving, and etc.) and the mobile device orientation and placement with respect to the user. After detecting the context information, a low-cost integrated positioning algorithm has been employed to estimate pedestrian navigation parameters. The method is based on the integration of the relative user’s motion (changes of velocity and heading angle) estimation based on the video image matching and absolute position information provided by GPS. A Kalman filter (KF) has been used to improve the navigation solution when the user is walking and the phone is in his/her hand. The Experimental results demonstrate the capabilities of this method for outdoor personal navigation systems.
Highlights
Due to the rapid developments in mobile computing, wireless communications and positioning technologies, using smartphones as a personal navigation system (PNS) is getting popular
The main drawback of the IMU is that they are based on the relative position estimation techniques and use the previous states of the system; after a short period of time low cost MEMS (Micro Electro-Mechanical Systems) sensors measurements typically result in large cumulative drift errors unless the error are bounded by measurements from other systems (Aggarwal et al, 2010)
Another solution is the vision-based navigation using video camera sensors. These systems are based on two main strategies: estimation of absolute position information using a priori formed databases which highly depends on the availability of image database for that area (Zhang and Kosecka, 2006) and estimating relative position information using the motion of the camera calculated from consecutive images which suffers from cumulative drift errors (Ruotsalainen et al, 2011; Hide et al, 2011)
Summary
Due to the rapid developments in mobile computing, wireless communications and positioning technologies, using smartphones as a PNS is getting popular. The main drawback of the IMU is that they are based on the relative position estimation techniques and use the previous states of the system; after a short period of time low cost MEMS (Micro Electro-Mechanical Systems) sensors measurements typically result in large cumulative drift errors unless the error are bounded by measurements from other systems (Aggarwal et al, 2010). Another solution is the vision-based navigation using video camera sensors. The contribution of this paper is to develop a visually-aided personal navigation solution using the smartphone embedded sensors which takes into account various user context
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