Multi sensor fusion for object detection using generalized feature models

This paper presents a multi sensor tmck- ing system and introduces the use of new generalized feature models. To detect and recognize objects as self- contained parts of the real world with two or more sen- sors of the same or of several types requires on the one hand fusion methods suitable for combining the data coming from the set of sensors in an optimal man- ner. This is realized by a sensor fusion principle on the basis of a Kalman filter. On the other hand it is necessary to model objects under the assumption that seveml sensors observe them. Therefore, we propose a new generalized 3D model which is suitable for this case. The paper presents a system for the detection and tracking of cars in road environments as an example. This system works with two sensors: a laser scanner and an infrared camera.