<title>Multisensor data fusion of points, line segments, and surface segments in 3D space</title>

Abstract

Recently, interest for multi sensor data, and consequently data fusion, for world modeling has been growing. Multi sensor data fusion is the process of matching and integrating data from multiple sensor input. The objectives of the geometric fusion process are to enhance accuracy and resolution in the world model and to improve the effectiveness and the robustness of the overall imaging system. A solid description and parametric representation of the extracted 3D features from sensor data, here referred to as geometric primitives, the building blocks of the world model, are fundamental to model sensor uncertainties and to integrate sensor data. Parametric representations of three types of geometric primitives (points, line segments, and surface segments in 3D space) are presented. Furthermore, given these parametric representations, the application of the (Extended) Kalman Filter for data association and fusion is described. Special attention is paid to the fusion of geometric primitives of different modality. The resulting integrated geometric primitives are stored in a 3D world model that can be used for tele-presence and autonomous system control purposes.