EU Long-term Dataset with Multiple Sensors for Autonomous Driving
This provides a new dataset for the autonomous driving research community, but it is incremental as it builds on existing sensor technology and data collection methods.
The paper introduces a new multisensor platform integrating eleven heterogeneous sensors and a ROS-based software for efficient and accurate environment perception in autonomous driving, and presents a publicly available dataset captured with this platform to address challenges like highly dynamic environments and long-term autonomy.
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.