Snail-Radar: A large-scale diverse dataset for the evaluation of 4D-radar-based SLAM systems
<|reference_start|>Snail-Radar: A large-scale diverse dataset for the evaluation of 4D-radar-based SLAM systems: 4D radars are increasingly favored for odometry and mapping of autonomous systems due to their robustness in harsh weather and dynamic environments. Existing datasets, however, often cover limited areas and are typically captured using a single platform. To address this gap, we present a diverse large-scale dataset specifically designed for 4D radar-based localization and mapping. This dataset was gathered using three different platforms: a handheld device, an e-bike, and an SUV, under a variety of environmental conditions, including clear days, nighttime, and heavy rain. The data collection occurred from September 2023 to February 2024, encompassing diverse settings such as roads in a vegetated campus and tunnels on highways. Each route was traversed multiple times to facilitate place recognition evaluations. The sensor suite included a 3D lidar, 4D radars, stereo cameras, consumer-grade IMUs, and a GNSS/INS system. Sensor data packets were synchronized to GNSS time using a two-step process: a convex hull algorithm was applied to smooth host time jitter, and then odometry and correlation algorithms were used to correct constant time offsets. Extrinsic calibration between sensors was achieved through manual measurements and subsequent nonlinear optimization. The reference motion for the platforms was generated by registering lidar scans to a terrestrial laser scanner (TLS) point cloud map using a lidar inertial odometry (LIO) method in localization mode. Additionally, a data reversion technique was introduced to enable backward LIO processing. We believe this dataset will boost research in radar-based point cloud registration, odometry, mapping, and place recognition.<|reference_end|>
@article{huai2024snail-radar:,
    title={Snail-Radar: A large-scale diverse dataset for the evaluation of
  4D-radar-based SLAM systems},
    author={Jianzhu Huai, Binliang Wang, Yuan Zhuang, Yiwen Chen, Qipeng Li,
  Yulong Han, Charles Toth},
    journal={arXiv preprint arXiv:2407.11705},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11705},
    primaryClass={cs.RO eess.SP}
}