Rapid State Augmentation for Compressed EKF-Based Simultaneous Localization and Mapping

A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping (CEKF-SLAM) algorithm was proposed. State augment usually requires a fully-updated state covariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient matrix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computational amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project.