This paper presents an efficient approach to outdoor visually augmented odometry. The technique computes relative pose constraints via a robust least squares minimisation of 3D point correspondences, which are in turn obtained from the matching of SIFT features over two consecutive image pairs. Pose constraints are then used to build a history of pose estimates with and incremental delayed-state information filter. The efficiency of the approach resides on the exact sparseness of the delayed-state information form used.