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Iterated SLSJF: A Sparse Local Submap Joining Algorithm with Improved Consistency

S. Huang; Z. Wang; G. Dissanayake; Udo Frese
In: Proceedings of the Australasian Conference on Robotics and Automation. Australasian Conference on Robotics and Automation (ACRA-08), December 3-5, Canberra, Australia, 2008.


This paper presents a new local submap joining algorithm for building large-scale feature based maps. The algorithm is based on the recently developed Sparse Local Submap Joining Fil- ter (SLSJF) and uses multiple iterations to im- prove the estimate and hence is called Iterated SLSJF (I-SLSJF). The input to the I-SLSJF algorithm is a sequence of local submaps. The output of the algorithm is a global map con- taining the global positions of all the features as well as all the robot start/end poses of the local submaps. In the submap joining step of I-SLSJF, when- ever the change of state estimate computed by an Extended Information Filter (EIF) is larger than a prede¯ned threshold, the information vector and the information matrix is recom- puted as a sum of all the local map contribu- tions. This improves the accuracy of the esti- mate as well as avoids the possibility that the Jacobian with respect to the same feature gets evaluated at di®erent estimate values, which is one of the major causes of inconsistency for EIF/EKF algorithms. Although the computa- tional cost of I-SLSJF is higher than that of SLSJF, the algorithm can still be implemented e±ciently due to the exactly sparseness of the information matrix. The new algorithm is com- pared with EKF SLAM and SLSJF using both computer simulation and experimental exam- ples.