  This paper presents a robust 6-DOF relative navigation by combining the
iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman
filter (AKF) in a closed-loop configuration together with measurements from a
laser scanner and an inertial measurement unit (IMU). In this approach, the
fine-alignment phase of the registration is integrated with the filter
innovation step for estimation correction while the filter estimate propagation
provides the coarse alignment needed to find the corresponding points at the
beginning of ICP iteration cycle. The convergence of the ICP point matching is
monitored by a fault-detection logic and the covariance associated with the ICP
alignment error is estimated by a recursive algorithm. This ICP enhancement has
proven to improve robustness and accuracy of the pose tracking performance and
to automatically recover correct alignment whenever the tracking is lost. The
Kalman filter estimator is designed so as to identify the required parameters
such as IMU biases and location of the spacecraft center-of-mass (CoM).
