A Evaluation Test Bed for Outdoor Localization Algorithms Using a High-Precision Positioning System

The localization of mobile robots is very important for any outdoor application such as search and rescue, reconnaissance, surveillance, and monitoring. Already a lot of research was done in the field of localization; nevertheless an accurate and reliable positioning of a mobile robot is very challenging. The common Global Positioning System (GPS) is a very common and popular for the outdoor localization. But GPS has well known drawbacks, like the limited accuracy of the positioning. For this reason, various sensor data fusion algorithms were developed, which fuse the GPS positioning information and dead reckoning sensors to overcome the drawbacks of GPS and dead reckoning sensors. A fundamental aspect of these fusions is the resulting accuracy of the determined position, which depends on the sensor accuracy as well as on the filter parameters. Usually, the output of such Filter are compared to the rare sensor data, but it is not compared to the real position of the vehicle. In the following, a test bed for localization methods will be introduced. To demonstrate the usage, Kalman Filter were implemented to fuse GPS and odometry sensors to determine the position of a mobile robot in an outdoor environment. The focus of this paper is the calibration and evaluation of the Kalman Filter using the test bed based on a high precision optical positioning system. Therefore, the experimental setup, the Kalman Filter, the synchronization of the high precision positioning system, and the transformation of one system into the other is explained.