Multilevel Datenfusion konkurrierender Sensoren in der Fahrzeugumfelderfassung

With the present thesis a contribution to the increase of the accuracy and reliability of a sensor-supported recognition and tracking of objects in a vehicle’s surroundings should be made. Based on a detection system, consisting of a stereo camera and a laser scanner, novel developed procedures are introduced for the whole processing chain of the sensor data. In addition, a new framework is introduced for the fusion of heterogeneous sensor data. By combining the data fusion results from the different processing levels the object detection can be improved. After a short description of the used sensor setup the developed procedures for the calibration and mutual orientation are introduced. As a result, the measuring error for the laser scanner can be reduced by over 10 %. With the segmentation of the spatial point data existing procedures are extended by the inclusion of measuring accuracy and specificity of the sensor. Furthermore, an approach for the determination of the minimally enclosing object box and the object alignment to support the object tracking is introduced. In the subsequent object tracking a new computation-optimized approach for the association of the related object hypotheses is presented. In addition, a model for a dynamic determination and tracking of an object reference point is described which exceeds the classical tracking of the object center in the track accuracy. By the introduced fusion framework it is possible to merge the sensor data at three different processing levels (point, object and track level). A sensor independent approach for the low fusion of point data is demonstrated which delivers the most precise object description in comparison to the other fusion levels and the single sensors. For the higher fusion levels new procedures were developed to discover and clean up the detection and processing mistakes benefiting from the competing sensor information. Finally it is described how the fusion results of the upper and lower levels can be brought together for an ideal object description. The effectiveness of the newly developed methods was checked either by simulation or in real measurement scenarios. The interdisciplinary standard of this thesis lies in the novel concept of a combined multi layer fusion of heterogeneous sensor data for a more reliable and more precise object description in the vehicle surrounding field. This concept was implemented in his totality with a technological innovative reference sensor system and tested successfully.