Multisensor aided inertial navigation in 6DOF AUVs using a Multiplicative Error State Kalman Filter

Abstract

Underwater autonomous robots with 6 degrees of freedom (DOF), equipped with low cost aided inertial navigation systems, usually make use of Kalman Filters (KF) to fuse, in a single vector, the measurements given by multiple sensors. In this context, Multiplicative Error State Kalman Filters (MESKF) are preferable than standard KFs to increase the reliability of the vehicle motion estimates. This particular design of KF can contain in its state vector, in addition to the pose and velocity, the biases of the acceleration and of the angular rate provided by inertial units.