  To navigate through urban roads, an automated vehicle must be able to
perceive and recognize objects in a three-dimensional environment. A high-level
contextual understanding of the surroundings is necessary to plan and execute
accurate driving maneuvers. This paper presents an approach to fuse different
sensory information, Light Detection and Ranging (lidar) scans and camera
images. The output of a convolutional neural network (CNN) is used as
classifier to obtain the labels of the environment. The transference of
semantic information between the labelled image and the lidar point cloud is
performed in four steps: initially, we use heuristic methods to associate
probabilities to all the semantic classes contained in the labelled images.
Then, the lidar points are corrected to compensate for the vehicle's motion
given the difference between the timestamps of each lidar scan and camera
image. In a third step, we calculate the pixel coordinate for the corresponding
camera image. In the last step we perform the transfer of semantic information
from the heuristic probability images to the lidar frame, while removing the
lidar information that is not visible to the camera. We tested our approach in
the Usyd Dataset \cite{usyd_dataset}, obtaining qualitative and quantitative
results that demonstrate the validity of our probabilistic sensory fusion
approach.
