  In this paper, we present INertial Lidar Localisation Autocalibration And
MApping (IN2LAAMA): an offline probabilistic framework for localisation,
mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most of
today's lidars collect geometric information about the surrounding environment
by sweeping lasers across their field of view. Consequently, 3D-points in one
lidar scan are acquired at different timestamps. If the sensor trajectory is
not accurately known, the scans are affected by the phenomenon known as motion
distortion. The proposed method leverages preintegration with a continuous
representation of the inertial measurements to characterise the system's motion
at any point in time. It enables precise correction of the motion distortion
without relying on any explicit motion model. The system's pose, velocity,
biases, and time-shift are estimated via a full batch optimisation that
includes automatically generated loop-closure constraints. The autocalibration
and the registration of lidar data rely on planar and edge features matched
across pairs of scans. The performance of the framework is validated through
simulated and real-data experiments.
