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The fusion of sensor data from heterogeneous sensors is crucial for robustperception in various robotics applications that involve moving platforms, forinstance, autonomous vehicle navigation. In particular, combining camera andlidar sensors enables the projection of precise range information of thesurrounding environment onto visual images. It also makes it possible to labeleach lidar point with visual segmentation/classification results for 3Dmapping, which facilitates a higher level understanding of the scene. The taskis however considered non-trivial due to intrinsic and extrinsic sensorcalibration, and the distortion of lidar points resulting from the ego-motionof the platform. Despite the existence of many lidar ego-motion correctionmethods, the errors in the correction process due to uncertainty in ego-motionestimation are not possible to remove completely. It is thus essential toconsider the problem a probabilistic process where the ego-motion estimationuncertainty is modelled and considered consistently. The paper investigates theprobabilistic lidar ego-motion correction and lidar-to-camera projection, whereboth the uncertainty in the ego-motion estimation and time jitter in sensorymeasurements are incorporated. The proposed approach is validated both insimulation and using real-world data collected from an electric vehicleretrofitted with wide-angle cameras and a 16-beam scanning lidar.

Probabilistic Egocentric Motion Correction of Lidar Point Cloud and Projection to Camera Images for Moving Platforms