The non-Gaussianity of the depth estimate uncertainty degrades the performance of monocular extended Kalman filter SLAM (EKF-SLAM) systems employing a 3-component Cartesian landmark parameterization, especially in low-parallax configurations. Even particle filter SLAM (PF-SLAM) approaches are affected, as they utilize EKF for estimating the map. The inverse depth parameterization (IDP) alleviates this problem through a redundant representation, but at the price of increased computational complexity. The authors show that such a redundancy does not exist in PF-SLAM, hence the performance advantage of the IDP comes almost without an increase in the computational cost. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Imre, E., & Berger, M. O. (2009). A 3-component inverse depth parameterization for particle filter SLAM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5748 LNCS, pp. 1–10). https://doi.org/10.1007/978-3-642-03798-6_1
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