Simultaneous localisation and map building using split covariance intersection

This paper develops a simultaneous localisation and map building (SLAM) algorithm which utilises the split covariance intersection (SCI) update rule. This algorithm decomposes estimates into a correlated component (whose precise structure is unknown) and a lower bound on an independent component. For a map of n beacons, the storage is O(n) and the computational costs are constant irrespective of map size. In a simple simulation example we show that the SCI algorithm, through exploiting a lower bound on independent information, performs substantially better than the traditional covariance intersection SLAM algorithm