RELLIS-3D is a multimodal dataset collected in off-road environments, containing 13,556 lidar scans and annotations of 6,235 images. The data was collected at the RELLIS campus of Texas A&M University and presents challenges to existing algorithms with regard to class imbalance and environmental topography. The dataset also provides stacked sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurements, and IMU data.

Citation

@misc{jiang2020rellis3d,
title={RELLIS-3D Dataset: Data, Benchmarks and Analysis},
author={Peng Jiang and Philip Osteen and Maggie Wigness and Srikanth Saripalli},
year={2020},
eprint={2011.12954},
archivePrefix={arXiv},
primaryClass={cs.CV}
}