Abstract
The popular extended Kalman filter SLAM (Simultaneous Localization andMapping) requires the uncertainty is Gaussian noise. This assumption is relaxed to bounded noise by the set membership SLAM. However, the published set membership SLAMs are not suitable for large-scale and on-line problems. In this paper, we use ellipsoid algorithm to SLAM problem. The proposed ellipsoid SLAM has advantages over EKF SLAM and the other set membership SLAM in noise requirement, on-line realization, and large-scale SLAM. By bounded ellipsoid technique, we analyze the convergence and stability of the novel algorithm. Simulation and experimental results are presented that the ellipsoid SLAM is effective for on-line and large-scale problems such as Victoria Park dataset.
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