Optimization of the simultaneous localization and map-building algorithm for real-time implementation

This paper addresses real-time implementation of the simultaneous localization and map-building (SLAM) algorithm. It presents optimal algorithms that consider the special form of the matrices and a new compressed filter that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors. It is shown that by extending the standard Kalman filter models the information gained in a local area can be maintained with a cost similar to O(Na-alpha(2)), where Na-alpha is the number of landmarks in the local area, and then transferred to the overall map in only one iteration at full SLAM computational cost. Additional simplifications are also presented that are very close to optimal when an appropriate map representation is used. Finally the algorithms are validated with experimental results obtained with a standard vehicle running in a completely unstructured outdoor environment.