Filter design for simultaneous localization and map building (SLAM)

This paper deals with the fusion of random variables when cross covariances are unknown. This is a vital problem in nearly every real world application since cross covariances are often impossible to obtain, but also cannot be ignored. We provide a rigorous derivation of the fusion equations which are also known as covariance intersection. This approach allows one to derive an iterative scheme for simultaneous mapping and localization. The algorithm can also be used for multi-robot explorations where highly correlated decentralized maps have to be fused to form a consistent global map. We show the mapping and localization results based on dense laser range scans.