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VISION-AIDED CONTEXT-AWARE FRAMEWORK FOR PERSONAL NAVIGATION 
SERVICES 
S. Saeedi*, A. Moussa, Dr. N. El-Sheimy 
Dept. of Geomatic Engineering, The University of Calgary, 2500, University Dr, NW, Calgary, AB, T2N 1N4 Canada 
— (ssaeedi, amelsaye, elsheimy) @ucalgary.ca 
Commission IV, WG 1V/5 
KEY WORDS: Navigation, Vision, Data mining, Recognition, Fusion, Video, IMU 
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. 
1. INTRODUCTION 
Due to the rapid developments in mobile computing, wireless 
communications and positioning technologies, using 
smartphones as a PNS is getting popular. This evolution has 
facilitated the development of applications that use the position 
of the user, often known as LBS. Using various sensors on 
smartphones provides a vast amount of information; however, 
finding a ubiquitous and accurate pedestrian navigation solution 
is a very challenging topic in ubiquitous positioning (Lee & 
Gerla, 2010; Mokbel & Levandoski, 2009). Position estimation 
in outdoor environments is mainly based on the global 
positioning systems (GPS) or assisted GPS (AGPS); however, it 
is a challenging task in indoor or urban canyon, especially when 
GPS signals are unavailable or degraded due to the multipath 
effect. In such cases, usually other navigation sensors and 
solutions are applied for pedestrians. The first alternative is 
wireless radio sensors, such as Bluetooth, RFID (Radio 
Frequency IDentification) or WLAN (Wireless Local Arca 
Network). These systems have limited availability and need a 
pre-installed infrastructure that restricts their applicability. The 
second navigation system is the IMU (Inertial Measurement 
Unit) sensors that provide a relative position based on the 
distance travelled and device’s orientation. The distance and 
orientation information can be measured with a gyroscope and 
an accelerometer sensor. 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; therefore, 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). Since there is not a single comprehensive 
sensor for indoor navigation, it is necessary to integrate the 
measurements from different sensors to improve the position 
information. 
Modern smartphones contain a number of Low cost MEMS 
sensors (e.g. magnetometer, accelerometer, and gyroscope) that 
can be used for integrated ubiquitous navigation even if GPS 
signals are unavailable. Vision sensors are ideal for PNS since 
they are available in good resolution on almost all smartphones. 
Therefore, in this research a vision sensor is used to capture the 
user's motion parameters using consecutive image frames and 
to provide navigation aid when measurements from other 
systems such as GPS are not available. This system doesn't 
need any special infrastructure and makes use of camera as an 
ideal aiding system. Since mobile users carry the device with 
different orientation and placement, in almost everywhere 
(indoor and outdoor environments) while doing various 
activities (such as walking, running and driving), using specific 
customized and context-aware algorithms are necessary for 
different users’ modes. Therefore, a mobile navigation 
application must be aware of user and device context to use 
appropriate algorithm for cach case. For example, when the 
context information shows that device is in “texting” or 
“talking” mode, the observation from camera can be integrated 
with GPS sensor to improve and validate the pedestrian dead- 
reckoning algorithm. The main issue in context-aware PNSs is 
detecting relevant context information using embedded mobile 
sensors in an implicit way. 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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