Vision-Based Localization and Data Fusion in a System of Cooperating Mobile Robots

The approach presented in this paper allows a team of mobile robots to estimate cooperatively their poses, i.e. positions and orientations, and the poses of other observed objects from images. The images are obtained by calibrated color cameras mounted on the robots. Model knowledge of the robots ' environment, the ge-ometry of observed objects, and the characteristics of the cameras are represented in curve functions which describe the relation between model curves in the im-age and the sought pose parameters. The pose pa-rameters are estimated by minimizing the distance be-tween model curves and actual image curves. Obser-vations from possibly dierent view points obtained at dierent times are fused by a method similar to the extended Kalman lter. In contrast to the extended Kalman lter, which is based on a linear approxima-tion of the measurement equations, we use an iterative optimization technique which takes non-linearities into account. The approach has been successfully used in robot soccer, where it reliably maintained a joint pose estimate for the players and the ball