Abstract:
This paper investigates the convergence properties
and consistency of Extended Kalman Filter (EKF) based simultaneous
localization and mapping (SLAM) algorithms. Proofs
of convergence are provided for the nonlinear two-dimensional
SLAM problem with point landmarks observed using a range-and bearing
sensor. It is shown that the robot orientation uncertainty
at the instant when landmarks are first observed has a significant
effect on the limit and/or the lower bound of the uncertainties of
the landmark position estimates. This paper also provides some
insights to the inconsistencies of EKF based SLAM that have been
recently observed. The fundamental cause of EKF SLAM inconsistency
for two basic scenarios are clearly stated and associated
theoretical proofs are provided.
