Towards a unified architecture for mapping static environments

Sensor fusion systems are becoming more and more important in automotive applications, especially for future driver assistance systems and autonomous driving. While it was sufficient to use single sensors in early driving assistance systems, future systems will rely on several sensors with different measurement principles. The probably conflicting measurements need to be fused to retrieve a complete picture of the current surroundings. However, an increased number of sensors leads to difficulties while incorporating their measurements, since every sensor provides its data via special interfaces. This inconvenience of current sensor fusion architectures has been solved for moving objects. A fusion architecture based on a generic interface - the object list - has been developed over the last years. It allows an easy use of additional sensors as long as they provide their measurements as object lists. Similar solutions for static environments are rare, but the recent proposal of the Fences-approach [1] is promising. This work shows, how sensors and occupancy grids are represented in the Fences-architecture. Steps and algorithms are presented to transform sensor reading into Fences as well as extracting Fences from common grid-based strategies for mapping static environments. The algorithms are applied to the conversion of ultrasonic sensor readings and LiDAR measurements to Fences. Furthermore, the construction of Fences from Bayesian occupancy grids is presented.