<title>Nonlinear attractor dynamics: a new approach to sensor fusion</title>

Abstract

Fusing information of multiple sensor is particularly difficult if the sensor systems which provide the information have very different characteristics such as different data formats, reliabilities, signal to noise ratios, sampling rates and so on. Furthermore, the information is often provided on different levels of abstraction such as the direct sensor output in contrast to expert knowledge or a priori information. We propose a new approach to sensor fusion which accounts for these problems. The basic idea is to represent the quantity to estimate as the state variable of a nonlinear dynamical system. The sensor signals act on this dynamics by specifying attractors with limited range of influence. The dynamics relaxes into a stable state which results from the superposition of the attractors. By means of the limited attractor ranges, the dynamics automatically averages nonlinearly over corresponding sensor signal while outliers stemming form temporarily de-calibrated or erroneous sensor are discarded. Self-calibration is achieved by representing also the sensor signals as dynamical states and specifying an attractor at the position of the fused estimate. By using the unified attractor representation, abstract information can be treated in the same way as direct sensor input. Furthermore a mathematically well defined and algebraically analyzable format for dynamic sensor information on various levels of abstraction is available. We verify our concept for the example of man-machine interaction: fusing visual and odometric sensor information for the autonomous position estimation with acoustic guidance information for the target acquisition of a mobile robot.