Intelligent Land Vehicle Navigation: Integrating Spatial Information into the Navigation Solution

Successful intelligent land vehicle navigation systems can only be realised through the integration of navigation data and spatial information. This is evident in the development of modern Intelligent Transportation Systems (ITS), where the Global Positioning System (GPS) is used to provide the navigation data, and spatial information contained within an information database is used to provide location details. With plans already underway for the development of a Global Navigation Satellite System (GNSS), the next generation of ITS will definitely incorporate satellite positioning technologies. Unfortunately, the performance of any satellite technology is restricted in areas where sky visibility is completely or partially obstructed. There is a fundamental requirement to provide a robust navigation system to support future developments of ITS. Potential solutions include the development of integrated systems, which combine measurements from GPS and other complementary sensors, such as dead reckoning (DR), to improve the continuity of positioning. However, current integration algorithms, such as Kalman filtering, have difficulty in contending with the high dynamics of land vehicles, and challenge the navigation capability of these systems within the environment of ‘urban canyons’. Ironically this is perhaps the one environment where the successful application of satellite technology could most benefit the ITS industry. This paper discusses the integration of the inherent intelligence of spatial information contained within a Geographical Information System (GIS) with measurements received from a navigation system. The spatial information provides additional data that is used to constrain the navigation solution and provide a more accurate and reliable position estimate. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. It enables the use of lower accuracy navigation devices, thereby reducing the cost of navigation systems while still providing a viable solution. INTRODUCTION Intelligent navigation is the process of improving the basic solution obtained from low cost navigation sensors for land mobile applications. This is achieved through the integration of measurements provided by the navigation instruments with additional spatial information contained within a map database. In the majority of current real-time vehicle navigation systems, a low cost GPS receiver is used to provide information on the vehicle’s position, and a Geographic Information System (GIS) is used to provide location details. For land vehicle navigation applications, GPS only systems are incapable of maintaining continuous navigation capability in environments where the satellite signals are obstructed (e.g. by buildings, trees etc). Solutions to this problem commonly involve the integration of GPS with dead reckoning (DR) sensors. This solution often increases the overall cost of the navigation system with little improvement in the solution, as DR systems suffer from the accumulation of errors over time. Additionally, complex Kalman filtering algorithms used for a more rigorous integration of GPS and DR measurements are often unable to cope with the high dynamics of land vehicle navigation. With the wealth of information contained in a GIS, data can easily be extracted and integrated into the vehicle navigation solution. In this way, apart from assuming a passive role of informing users about objects of interest in their surroundings, the information contained in the database is used as additional measurements within the navigation solution. This type of integration offers a solution that is capable of improving the accuracy and performance of low cost, low precision sensors for urban land vehicle navigation. DESIGNING A NAVIGATION SYSTEM The intelligent land vehicle navigation system developed for this research consists of both hardware and software components. The real-time navigation hardware component consists of: ∗ a low cost Garmin GPS receiver; ∗ a KVH fibre optic gyro (FOG); ∗ a Pentium 133, 64 megabytes laptop computer; ∗ an odometer. The software module developed in Smallworld Magik and Microsoft Visual Basic provides a user interface to the navigation software, a means of accessing the GIS database, as well as enabling intelligent navigation through the integration of measurements from the GIS with those from the real-time navigation system. The Hardware Components The system developed for this project is modular in its design. It therefore enables easy integration with various types of navigation instruments and techniques. Three modes of navigation are tested within this research: ∗ satellite navigation; ∗ DR navigation; ∗ combined GPS/DR navigation. The satellite navigation mode relies solely on the GPS receiver. With the recent removal of selective availability (SA), the position data obtained from the GPS receiver is accurate to ±12 meters 95% of the time (Hooper, 2000). The Garmin GPS 45 receiver used can track up to eight satellites simultaneously, supports the National Marine Electronics Association (NMEA) 0183 electrical interface and data protocol standard for communication between marine instrumentation, and has an RS-232 serial communication output (Garmin International, 1994). The specific NMEA sentences used by the navigation system were the Recommend Minimum Specific GPS/TRANSIT Data (RMC) and Global Positioning System Fix Data (GGA) sentences. The DR navigation mode utilises the change in the vehicle direction measurements from a KVH FOG and distance measurements from the vehicle’s odometer. The FOG has an RS232 serial communication output at 9600 baud and is capable of measuring a maximum rotation rate of ±100°/second (KVH Industries, Inc., 1999). The FOG allows for input from a vehicle’s odometer in the form of electrical pulses. Each pulse represents an amount of wheel rotation predetermined by the vehicle manufacturer. Data received from the odometer is converted into binary format and included with the information transmitted via the FOG’s RS-232 output. This data is then used to compute distance travelled by the vehicle. The accuracy of the DR system is limited predominantly by distance measurement and is approximately 2% of the distance travelled. Since the DR system contains no means of absolute positioning, navigation requires the provision of a starting location and direction. The combined navigation mode integrates both the GPS and DR sensors. In this research, because of the high relative positioning accuracy offered by the DR sensors, the navigation system relies primarily on DR, resorting to the GPS navigation solution only when the difference between independently measured GPS and DR positions agree to an expected level. To define this tolerance, the DR and GPS accuracies were taken into account. Given that the major error accumulation in DR measurements is from distance measurement and that the GPS measurement is accurate to ±12m 95% of the time, the difference between DR and GPS position calculations should be within: ± (12m + 2% of distance travelled since last GPS measurement used) A flow diagram of the system hardware and data flow is depicted in Figure 1. Unlike the GPS receiver, the FOG does not constitute a low cost instrument. It was used initially to implement and refine the models for intelligent navigation. However, subsequent testing described in this paper will show that with intelligent navigation, such high accuracy devices are not required. The Software Component Implementation of the intelligent navigation system required a platform to provide a user interface to the navigation software, to facilitate the data integration between the different hardware, and to analyse and display spatial data. Smallworld 3 GIS was chosen for this purpose. Smallworld’s open architecture and comprehensive spatial analysis functionality offered significant benefits in developing the software component of this project. The programming language of Smallworld 3 GIS is Magik, an object-oriented programming language that is also used to implement the majority of the core Smallworld 3 GIS product itself. Smallworld 3 GIS includes facilities for integrating applications programmed in languages other than Magik. This was a particular advantage as it enabled interpretation of the navigation device outputs to take place in Microsoft Visual Basic. While Magik could have been used instead, Microsoft Visual Basic contains comprehensive serial communication libraries that aided development in the communication between Smallworld 3 GIS and the GPS receiver and FOG. A flow diagram of the data through the navigation system software is shown in Figure 2. GPS Receiver FOG Odometer Laptop NMEA sentences Wheel rotation pulses