This contribution introduces a three pillar information storage and management system for modeling the environment of autonomous systems. The main characteristics is the separation of prior knowledge, environment model and sensor information. In the center of the system is the environment model, which provides the autonomous system with information about the current state of the environment. It consists of instances with attributes and relations as virtual substitutes of entities (persons and objects) of the real world. Important features are the representation of uncertain information by means of Degree-of-Belief (DoB) distributions, the information exchange between the three pillars as well as creation, deletion and update of instances, attributes and relations in the environment model. In this work, a Bayesian method for fusing new observations to the environment model is introduced. For this purpose, a Bayesian data association method is derived. The main question answered here is the observation-to-instance mapping and the decision mechanisms for creating a new instance or updating already existing instances in the environment model

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This contribution presents a fusion method for combined stereo and spectral series gained with a camera array with the main purpose of obtaining 3D information. In order to register the images, regions are used as invariant elements with respect to varying gray values. The approach uses their characteristics like size, position and shape for registration. The regions are identified employing the watershed transformation. The fusion problem is modeled with energy functionals that are to be optimized. Using the implemented algorithm, several scenes have been reconstructed. The experimental results show that the proposed method delivers reliable and accurate dense depth maps

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