  This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM)
for autonomous driving vehicles. Fusing data from landmark sensors and a
strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF)
plus the observability of the system are investigated. In addition to the
vehicle's states and landmark positions, a self-tuning filter estimates the IMU
calibration parameters as well as the covariance of the measurement noise. The
discrete-time covariance matrix of the process noise, the state transition
matrix, and the observation sensitivity matrix are derived in closed-form
making them suitable for real-time implementation. Examining the observability
of the 3D SLAM system leads to the conclusion that the system remains
observable upon a geometrical condition on the alignment of the landmarks.
