MSC-RAD4R: ROS-Based Automotive Dataset With 4D Radar

摘要

Recently, several 4D radar datasets have been published due to its high resolution and robustness in extreme weather conditions. However, most of the recent 4D radar dataset publications have insufficient odometry sensors, which is disadvantageous for 4D radar odometry research. To tackle this problem, this letter presents a 4D radar dataset with various odometry sensors based on a robot operating system (ROS) framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSC-RAD4R</i> , which stands for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Motivated for SLAM in City, ROS-based Automotive Dataset with 4D Radar</i> . Our dataset at a glance includes 98,786 pairs of stereo images, 60,562 frames of LiDAR data, 90,864 frames of 4D radar data, 60,570 frames of RTK-GPS, 60,559 frames of GPS, 1,211,486 frames of IMU data and 6,057,276 wheel data covering approximately 51.6 km and 100 minutes of automotive data in various environments including day, night, snow and smoke. In particular, our setup includes a high-resolution 79 GHz Adaptive PDM FMCW Oculii 4D radar, which provides approximately 5,000–20,000 points per frame and operates at a long range of 400 meters with a frequency of 15 Hz. It is expected that the proposed dataset will be useful for researchers working on 4D radar SLAM.