"Collective Localization: A distributed Kalman filter approach to localization of groups of mobile robots "
This paper presents a new approach to the coopera tive localization problem, namely collective localization. A group of M robots is viewed as a single system composed of robots that carry, in general, different sensors and have different positioning capabilities. A single Kalman filter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equa tions of the centralized Kalman filter are treated so that this filter can be decomposed in M modified Kalman filters each running on a separate robot. The collective localiza tion algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.