Publication
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.
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.