An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

Denis Gingras (Université de Sherbrooke, Canada)
DOI: 10.4018/978-1-60566-338-8.ch012

Abstract

In this chapter, the authors will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, they will look at the mathematical framework and technologies involved to design positioning systems. The authors will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. They will then look at the case of robustness of the positioning system when one or some of the sensors are faulty and will describe how the positioning system can be made more robust and adaptive in order to take into account the occurrence of faulty or degraded sensors. Finally, they will go one step further and explore possible architectures for collaborative positioning systems, whereas many vehicles are interacting and exchanging data to improve their own position estimate. The chapter is concluded with some remarks on the future evolution of the field.
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Introduction

The field of automotive positioning systems has become a research topic in full rise these last few years. Generally speaking, positioning information in road transportation is of prime importance for safety reasons. We need to know where we are (vehicle position) and we also need to locate obstacles and other objects/vehicles in the vicinity and nearby environment of our own vehicle. So an approach able to precisely localize our vehicle and evaluate its environment on the road is required to increase safety (Drane & Rizos, 1997). Apart from safety issues, the next generation of vehicles will allow the driver and passengers to have access to a broad range of services, such as path planning, navigation, guidance and tracking, which are based on information technologies, telecommunications and telematics. Future vehicles are likely to be as well mobile offices, information centers on wheel, or e-nodes connected to the web and other networks (Kim, Lee, Choi & al., 2003). To supply those services and to provide the adequate information contents to the driver and passengers, it is required to determine in real-time the vehicle’s position as accurately and efficiently as possible. This function is an essential part of an integrated navigation information system (INIS). An INIS embedded in a vehicle is basically composed of a geographic information system (GIS), a database composed of roadmaps, cartographic and geo-referenced data, a positioning module, a human-machine interface (HMI) as well as computing and telecommunication capabilities. It provides useful functionalities to the driver like path planning, guidance, digital map and points of interest directory (Farrell & Barth, 1999). The guidance module uses a planned trip to indicate the driver which route to take. To avoid giving wrong indications and impair driving safety, the navigation system relies upon a positioning module to know precisely and continuously the localization of the vehicle. In this chapter, we will focus on the positioning module of INISs.

Nowadays, the most popular sensor used for positioning is a global positioning system (GPS), also called a GPS receiver. However, GPS alone based systems suffer from various problems such as poor data latency, signal multipath errors and occlusions of satellite signals. To alleviate these problems and obtain the required performance of the positioning module, we usually use a combination of heterogeneous and complementary sensors whose measurements are integrated or fused in real time (Cannon, Nayak & al., 2001, Grewal, Weill & Andrews, 2001). Among the other types of sensors, we have Distance Measurement Indicators (DMI), such as differential odometers, and 2 or 3 axis inertial measurement units (IMU). Two or more of these complementary positioning sensing methods must be integrated together to achieve the required performance at low cost. The data integration, which implies the fusion of noisy signals provided by each sensor, must be performed in some optimal manner. Basically the goal of data fusion in positioning systems is 1) to fill in the time gaps whenever we face a loss of position estimate and 2) to improve the position estimate by ensemble averaging and exploiting information redundancy. Historically, data fusion for positioning has been applied in robotics (Roumeliotis & Bekey, 2000, Durant-Whyte, 1994) as well as in military, space and avionic applications (Suresh & Sivan, 2004). Multi-sensor data fusion system design based on rapid prototyping software platform and Kalman filtering for land navigation has also been proposed (Abuhadrous, Nashashibi, Laurgeau & Chinchole, 2004, Redmill, Martin & Özgüner, 2000). Hence, with the advent of more powerful, accessible and cheaper technologies embedded in vehicles for road transportation, on-board navigation systems are becoming feasible and standard in the automotive industry.

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