  In this work, we present LIBRE: LiDAR Benchmarking and Reference, a
first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a
range of manufacturers, models, and laser configurations. Data captured
independently from each sensor includes three different environments and
configurations: static targets, where objects were placed at known distances
and measured from a fixed position within a controlled environment; adverse
weather, where static obstacles were measured from a moving vehicle, captured
in a weather chamber where LiDARs were exposed to different conditions (fog,
rain, strong light); and finally, dynamic traffic, where dynamic objects were
captured from a vehicle driven on public urban roads, multiple times at
different times of the day, and including supporting sensors such as cameras,
infrared imaging, and odometry devices. LIBRE will contribute to the research
community to (1) provide a means for a fair comparison of currently available
LiDARs, and (2) facilitate the improvement of existing self-driving vehicles
and robotics-related software, in terms of development and tuning of
LiDAR-based perception algorithms.
