  Vision based localization is a popular approach to carry out manoeuvres
particularly in GPS-restricted indoor environments, because vision can
complement other activities performed by the robot. The objective is to
estimate the current location with respect to a known location by matching the
bearings. The problem is challenging as the known location information is in
terms of the bearings of landmarks extracted from an image. We address the
problem under more challenging scenario when landmarks are semi-static. In this
work, an observer formulation is presented which enables to incorporate the
effect of change in landmark position as parameters. The efficacy of two
estimators: Augmented Extended Kalman Filter (A-EKF) and a
Proportional-Integral EKF (PI-EKF) is tested under the cases where there are
changes in some of the landmark positions. Morever, it is likely that not all
landmarks are visible to the robot at all instants of time. A multi-rate
estimation framework is proposed to mitigate this issue. Observability analysis
is carried out to arrive upon a minimum number of landmarks required for such a
formulation. Simulation studies are presented to test the efficacy of the
formulations.
