  The field of autonomous driving has grown tremendously over the past few
years, along with the rapid progress in sensor technology. One of the major
purposes of using sensors is to provide environment perception for vehicle
understanding, learning and reasoning, and ultimately interacting with the
environment. In this paper, we first introduce a multisensor platform allowing
vehicle to perceive its surroundings and locate itself in a more efficient and
accurate way. The platform integrates eleven heterogeneous sensors including
various cameras and lidars, a radar, an IMU (Inertial Measurement Unit), and a
GPS-RTK (Global Positioning System / Real-Time Kinematic), while exploits a ROS
(Robot Operating System) based software to process the sensory data. Then, we
present a new dataset (https://epan-utbm.github.io/utbm_robocar_dataset/) for
autonomous driving captured many new research challenges (e.g. highly dynamic
environment), and especially for long-term autonomy (e.g. creating and
maintaining maps), collected with our instrumented vehicle, publicly available
to the community.
