Iterated SLSJF: A Sparse Local Submap Joining Algorithm with Improved Consistency
| Type of publication: | Inproceedings |
| Citation: | huang08 |
| Booktitle: | Proceedings of the Australasian Conference on Robotics and Automation, Canberaa |
| Year: | 2008 |
| Abstract: | 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. |
| Userfields: | pdfurl={http://www.informatik.uni-bremen.de/agebv/downloads/published/huang_acra_08.pdf}, project={A7-FreePerspective}, status={Reviewed}, |
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