Fusion of Map and Sensor Data in a Modern Car Navigation System

The main tasks of car navigation systems are positioning, routing, and guidance. This paper describes a novel, two-step approach to vehicle positioning founded on the appropriate combination of the in-car sensors, GPS signals, and a digital map. The first step is based on the application of a Kalman filter, which optimally updates the model of car movement based on the in-car odometer and gyroscope measurements, and the GPS signal. The second step further improves the position estimate by dynamically comparing the continuous vehicle trajectory obtained in the first step with the candidate trajectories on a digital map. This is in contrast with standard applications of the digital map where the current position estimate is simply projected on the digital map at every sampling instant.

[1]  Elliott D. Kaplan.  Understanding GPS : principles and applications , 1996 .

[2]  R. R. Joshi,et al.  A new approach to map matching for in-vehicle navigation systems: the rotational variation metric , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[3]  Hugh F. Durrant-Whyte,et al.  Inertial navigation systems for mobile robots , 1995, IEEE Trans. Robotics Autom..

[4]  Yilin Zhao,et al.  Vehicle Location And Navigation Systems , 1997 .

[5]  Paolo Toth,et al.  Models, relaxations and exact approaches for the capacitated vehicle routing problem , 2002, Discret. Appl. Math..

[6]  Hugh F. Durrant-Whyte,et al.  A high integrity IMU/GPS navigation loop for autonomous land vehicle applications , 1999, IEEE Trans. Robotics Autom..

[7]  M. S. Grewal,et al.  Application of Kalman filtering to the calibration and alignment of inertial navigation systems , 1991 .

[8]  Chen zewang,et al.  Development of an algorithm for car navigation system based on Dempster-Shafer evidence reasoning , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[9]  Frederick E. Petry,et al.  Principles and Applications , 1997 .

[10]  Ca Scott.  Improved positioning of motor vehicles through secondary information sources , 1996 .

[11]  Geoffrey Blewitt,et al.  Mapping Dilution of Precision (MDOP) and map-matched GPS , 2002, Int. J. Geogr. Inf. Sci..

[12]  Stefan Edelkamp,et al.  Route Planning and Map Inference with Global Positioning Traces , 2003, Computer Science in Perspective.

[13]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[14]  Gyu-In Jee,et al.  Efficient use of digital road map in various positioning for ITS , 2000, IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062).

[15]  Paolo Toth,et al.  The Vehicle Routing Problem , 2002, SIAM monographs on discrete mathematics and applications.