We offer a high performance map matching library for both embedded devices (thick clients) and well as server based (thin clients) solutions.Find out more
What is Map Matching?
Our algorithms and software modules have been used and approved in many field tests and industrial projects, e.g. in the German road pricing project.
Any global satellite positioning system (GNSS) like GPS, GALILEO or GLONASS , which is used for real-time positioning includes inevitable errors. Additionally, there's no (and will be no) absolutely accurate digital street map representing the real world. Therefore, in order to show a vehicle's position on a digital street map you have to match ("snap") its coordinates (received from a global positioning system) on top of a vector based street map. This challenging process is called "Map Matching".
Utilizing our UMDB and MicroMap frameworks we can offer a powerful map matching software that runs virtually on any vector-based map. We do not rely on map providers like Navteq, Tele Atlas, AND, or other commercial map vendors. We even support open source maps like OSM.
Personal navigation devices use on-board map matching algorithms to calculate the "true" vehicle position using a digital vector-based street map. The integration of additional hardware sensors makes a positioning device more expensive and prohibits simple turn-key products. But even with very good sensors, an initial sensor calibration and continuous sensor fusion these inaccuracies can't be avoided.
Smart Map Matching
Since both the user's location and the underlying road network is not 100% accurate, you have to snap the location obtained from e.g. a GPS receiver to the nearest node or edge in the vector map. In general, we know Point-to-Point, Point-to-Curve and Curve-to-Curve map matching algorithms. Of course, today these basic methods are improved and refined by considering rich map attributes like road class, turn restrictions, lane information, etc.
We've significantly improved the quality of our map matching algorithm by applying a configurable vehicle model which reflects the physical characteristics of a vehicle.
Our map matching algorithm continuously reads incoming GPS data and checks each position information whether it complies with the underlying physical vehicle model.