Snail Radar Dataset
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.
Brief description
A dataset collected by 4D radars, stereo cameras, and lidars. The preprint paper describing the dataset is at arXiv.
@misc{huai2024snailradar,
title={Snail-Radar: A large-scale diverse dataset for the evaluation of 4D-radar-based SLAM systems},
author={Jianzhu Huai and Binliang Wang and Yuan Zhuang and Yiwen Chen and Qipeng Li and Yulong Han},
year={2024},
eprint={2407.11705},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2407.11705},
}
Quick start
The dataset can be downloaded from either OneDrive or Baidu Netdisk(百度网盘) 提取码: 3141.
We provided zipped rosbags and zipped data folders.
The 44 sequences of our dataset are repeatedly collected at 8 places with three platforms in diverse weather/lighting conditions. The place shorthands bc=basketball court, sl=starlake, ss=software school, if=info faculty, iaf=info and arts faculty, iaef=info, arts, and engineering faculty, st=starlake tower, 81r=August 1 road. The platforms H=handheld, E=ebike, S=SUV. The weather and lighting condition is clear and daytime unless specified. Different to other sequences of the info and arts faculty, the 20240113/2 sequence's route is anticlockwise.
Place Setup | Date/Run | Dist.(m)/Dur.(sec) | Weather/lighting | |
---|---|---|---|---|
bc | H | 20230920/1 | 84/74 | light rain, night |
H | 20230921/2 | 59/83 | mod. rain | |
E | 20231007/4 | 78/51 | ||
E | 20231105/6 | 251/144 | mod. rain | |
E | 20231105_aft/2 | 167/104 | light rain | |
sl | H | 20230920/2 | 2182/1839 | light rain, night |
H | 20230921/3 | 2171/1857 | mod. rain | |
H | 20230921/5 | 2015/1731 | mod. rain | |
E | 20231007/2 | 1997/657 | ||
E | 20231019/1 | 1919/460 | night | |
E | 20231105/2 | 2045/524 | heavy rain | |
E | 20231105/3 | 2069/537 | heavy rain | |
E | 20231105_aft/4 | 2019/698 | light rain | |
E | 20231109/3 | 1983/546 | ||
ss | H | 20230921/4 | 736/622 | light rain |
E | 20231019/2 | 781/533 | night | |
E | 20231105/4 | 895/395 | heavy rain | |
E | 20231105/5 | 967/400 | mod. rain | |
E | 20231105_aft/5 | 826/534 | light rain | |
E | 20231109/4 | 795/533 | ||
if | S | 20231208/4 | 2228/515 | |
S | 20231213/4 | 2227/494 | light rain, night | |
S | 20231213/5 | 2225/475 | light rain, night | |
S | 20240115/3 | 2223/525 | dusk | |
S | 20240116/5 | 2228/514 | dusk | |
S | 20240116_eve/5 | 2224/462 | night | |
S | 20240123/3 | 2231/535 | ||
iaf | S | 20231201/2 | 4620/1042 | |
S | 20231201/3 | 4631/946 | ||
S | 20231208/5 | 4616/870 | ||
S | 20231213/2 | 4603/938 | light rain, night | |
S | 20231213/3 | 4613/875 | light rain, night | |
S | 20240113/2 | 4610/1005 | anticlockwise | |
S | 20240113/3 | 4613/962 | ||
S | 20240116_eve/4 | 4609/868 | night | |
iaef | S | 20240113/5 | 7283/1509 | |
S | 20240115/2 | 6641/1374 | ||
S | 20240116/4 | 6648/1515 | ||
st | S | 20231208/1 | 275/147 | |
S | 20231213/1 | 276/126 | light rain, night | |
S | 20240113/1 | 541/214 | ||
81r | S | 20240116/2 | 8554/1433 | light rain |
S | 20240116_eve/3 | 8521/1293 | night | |
S | 20240123/2 | 8539/1743 |
A more detailed table for these sequences with additional attributes can be found here.
SDK Tools
The SDK tools to load, visualize, convert the dataset sequences for ROS1 bags and file folders are available here.
Software Programs Developed for this Dataset
During creation this dataset, we have developed several useful tools.
Time offset and rotation calibration tool
Time offset and rotation calibration tool in matlab is released at here. The core of the algorithm is FFT-based correlation.
Cascaded pose graph optimization
Cascaded pose graph optimization is released at here. The core of the algorithm is weighted pose graph optimization. Features include right invariant pose error, IMU preintegration factor, and ceres solver.
These programs are made strong by running through hundreds of trials set up by this large dataset.
Issues
Please post the issues at the github repo issue tracker for this website source repo. We are working like cattle and horses to keep up with you.
License
This SNAIL radar dataset is made available under the Open Database License. Any rights in individual contents of the dataset are licensed under the Database Contents License.