Kalman Filter Based Dual Estimation:
Currently we are investigating a method for terrain aided navigation of
unmanned vehicles in unknown environments. The task is to simultaneously
estimate the state of the vehicle (position and attitude) and a map of the
surrounding environment given limited sensing capabilities. Possible
available sensors include an on-board inertial measurement unit (IMU) and
other simple “terrain sensors” such as altitude, temperature, or vision
based features. Explicit positioning sensors such as GPS or a prior land
map are not available. This problem is widely known as Simultaneous
Localization and Mapping (SLAM). A dual Kalman filter framework is
proposed that works by alternating between using one Kalman filter to
localize the vehicle given the current estimated map, and a second Kalman
filter to update the estimate of the map given the position of the
vehicle. Simulation results are generated for a two dimensional
environment comparing the Extended Kalman filter (EKF) and Sigma Point
Kalman filter (SPKF) based implementations. It is shown that the SPKF
based approach converges faster and also to a lower Mean Square Error
(MSE) than that of the EKF counterpart.