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.
- Pub Date:
- March 2020
- Computer Science - Robotics;
- Computer Science - Computer Vision and Pattern Recognition
- Accepted for oral presentation at IEEE Intelligent Vehicles Symposium (IV2020), https://2020.ieee-iv.org/ LIBRE dataset available at https://sites.google.com/g.sp.m.is.nagoya-u.ac.jp/libre-dataset/ Reference video available at https://youtu.be/rWyecoCtKcQ