  The fusion of sensor data from heterogeneous sensors is crucial for robust
perception in various robotics applications that involve moving platforms, for
instance, autonomous vehicle navigation. In particular, combining camera and
lidar sensors enables the projection of precise range information of the
surrounding environment onto visual images. It also makes it possible to label
each lidar point with visual segmentation/classification results for 3D
mapping, which facilitates a higher level understanding of the scene. The task
is however considered non-trivial due to intrinsic and extrinsic sensor
calibration, and the distortion of lidar points resulting from the ego-motion
of the platform. Despite the existence of many lidar ego-motion correction
methods, the errors in the correction process due to uncertainty in ego-motion
estimation are not possible to remove completely. It is thus essential to
consider the problem a probabilistic process where the ego-motion estimation
uncertainty is modelled and considered consistently. The paper investigates the
probabilistic lidar ego-motion correction and lidar-to-camera projection, where
both the uncertainty in the ego-motion estimation and time jitter in sensory
measurements are incorporated. The proposed approach is validated both in
simulation and using real-world data collected from an electric vehicle
retrofitted with wide-angle cameras and a 16-beam scanning lidar.
