RELLIS-3D: RGB - Dataset Ninja

Introduction #

Peng Jiang, Philip Osteen, Maggie Wignesset al.

The authors of the RGB part of a RELLIS-3D: A Multi-modal Dataset for Off-Road Robotics address the critical need for semantic scene understanding in off-road environments to ensure robust and safe autonomous navigation. Recognizing the scarcity of multimodal off-road data in existing autonomy datasets, they present RELLIS-3D—a multimodal dataset collected in an off-road setting, specifically on the Rellis Campus of Texas A&M University. This dataset includes annotations for 13,556 LiDAR scans and 6,235 images, introducing challenges related to class imbalance and environmental topography for existing algorithms. Furthermore, the authors conduct evaluations of state-of-the-art deep learning semantic segmentation models on RELLIS-3D, revealing the unique challenges it poses compared to datasets focused on urban environments. The dataset aims to provide researchers with the resources necessary to advance algorithms and explore new research directions for enhancing autonomous navigation in off-road scenarios.

Warthog Platform Configuration. Illustration of the dimensions and mounting positions of the sensors with respect to the robot body. (Units: cm)

The sensor setup and calibration involve various instruments:

1 × Ouster OS1 LiDAR: 64 Channels, 2048 horizontal resolution, 10 Hz, 45◦ vertical field of view.
1 × Velodyne Ultra Puck: 32 Channels, 10hz, 40◦ vertical field of view.
1 × Nerian Karmin2 + Nerian SceneScan: 3D Stereo Camera, 10 hz.
1 × RGB Camera: Basler acA1920-50gc camera with 16mm/F18 EDMUND Optics lens, image resolution 1920x1200, 10 hz.
Inertial Navigation System (GPS/IMU): Vectornav VN-300 Dual Antenna GNSS/INS, 300 Hz GPS, 100 Hz IMU.

The platform, Warthog, features two computers—one for robotic control and another for data collection and sensor processing. The synchronization of sensors and computers is achieved using Precision Time Protocol (PTP). Camera calibration is performed using the ROS Camera Calibrator library for intrinsic camera calibration, and the extrinsic calibration between sensors is determined using established methods.

Ground truth annotations examples provided in the RELLIS-3D dataset. Images are densely annotated with pixel-wise labels from 20 different visual classes. LiDAR scans are point-wise labeled with the same ontology.

RELLIS-3D consists of five traversal sequences recorded on non-paved trails of the Ground Research facility, each presenting distinct environmental challenges. The dataset ontology includes object and terrain classes, derived from the RUGD dataset, but expanded to encompass unique classes relevant to off-road scenarios. Annotations were provided by Appen through crowdsourcing, with trained annotators ensuring consistency. Both pixel-wise image annotations and point-wise annotations for 3D point clouds are included in the dataset. The authors highlight the statistics of the dataset, emphasizing the imbalanced class distribution, which is common in datasets used for semantic segmentation, but is more severe in off-road environments compared to urban settings.

The authors present detailed statistics on class distribution for both image and point cloud annotations, shedding light on the challenges posed by the unique characteristics of off-road environments.

Left - Image Label distribution. The sky, grass, tree and bush constitute the major classes. Right - Point Cloud Label distribution. The grass, tree, and bush also dominate the population.

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Research Paper

GitHub

Summary #

RELLIS-3D: A Multi-modal Dataset for Off-Road Robotics (RGB) is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the robotics industry.

The dataset consists of 5957 images with 200052 labeled objects belonging to 19 different classes including grass, sky, bush, and other: tree, mud, concrete, barrier, pole, vehicle, puddle, building, object, rubble, person, fence, asphalt, log, water, and dirt.

Images in the RELLIS-3D: RGB dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (3302 images), test (1672 images), and val (983 images). The dataset was released in 2020 by the Texas A&M University and CCDC Army Research Laboratory.

Here are the visualized examples for the classes:

Explore #

RELLIS-3D: RGB dataset has 5957 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from RELLIS-3D: RGB

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Have a look at 5957 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 19 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-10 of 19

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
grass➔ mask	5955	29603	4.97	34.23%
sky➔ mask	5889	57595	9.78	29.43%
bush➔ mask	5859	20063	3.42	17.16%
tree➔ mask	5733	21115	3.68	14.82%
mud➔ mask	3859	18847	4.88	3.53%
concrete➔ mask	2605	11812	4.53	3.56%
barrier➔ mask	2211	9590	4.34	1%
pole➔ mask	1965	4521	2.3	0.06%
vehicle➔ mask	1718	5241	3.05	0.13%
puddle➔ mask	1630	6033	3.7	2.67%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-10 of 19

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
sky mask	57595	3.01%	78.32%	0%	2px	0.17%	1083px	90.25%	85px	7.07%	1px	0.05%	1920px	100%
grass mask	29603	6.89%	84.2%	0%	1px	0.08%	1200px	100%	164px	13.63%	1px	0.05%	1920px	100%
tree mask	21115	4.02%	61.81%	0%	1px	0.08%	1180px	98.33%	170px	14.13%	2px	0.1%	1920px	100%
bush mask	20063	5.01%	94.07%	0%	1px	0.08%	1200px	100%	218px	18.2%	2px	0.1%	1920px	100%
mud mask	18847	0.72%	23.28%	0%	3px	0.25%	745px	62.08%	126px	10.47%	2px	0.1%	1920px	100%
concrete mask	11812	0.78%	23.27%	0%	1px	0.08%	504px	42%	57px	4.73%	2px	0.1%	1920px	100%
barrier mask	9590	0.23%	23%	0%	3px	0.25%	615px	51.25%	38px	3.15%	3px	0.16%	1206px	62.81%
puddle mask	6033	0.72%	36.42%	0%	5px	0.42%	621px	51.75%	77px	6.43%	7px	0.36%	1920px	100%
vehicle mask	5241	0.04%	0.36%	0%	3px	0.25%	110px	9.17%	26px	2.17%	3px	0.16%	311px	16.2%
fence mask	4583	0.05%	2.08%	0%	4px	0.33%	559px	46.58%	73px	6.11%	2px	0.1%	797px	41.51%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 101223 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 101223

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	grass mask	frame002306-1581624020_950.jpg	1200 x 1920	654px	54.5%	1920px	100%	29.95%
2➔	grass mask	frame002306-1581624020_950.jpg	1200 x 1920	17px	1.42%	40px	2.08%	0.03%
3➔	grass mask	frame002306-1581624020_950.jpg	1200 x 1920	17px	1.42%	38px	1.98%	0.03%
4➔	tree mask	frame002306-1581624020_950.jpg	1200 x 1920	97px	8.08%	299px	15.57%	0.93%
5➔	tree mask	frame002306-1581624020_950.jpg	1200 x 1920	116px	9.67%	661px	34.43%	1.97%
6➔	tree mask	frame002306-1581624020_950.jpg	1200 x 1920	113px	9.42%	943px	49.11%	3.26%
7➔	sky mask	frame002306-1581624020_950.jpg	1200 x 1920	489px	40.75%	1920px	100%	36.72%
8➔	person mask	frame002306-1581624020_950.jpg	1200 x 1920	119px	9.92%	48px	2.5%	0.17%
9➔	person mask	frame002306-1581624020_950.jpg	1200 x 1920	108px	9%	44px	2.29%	0.11%
10➔	person mask	frame002306-1581624020_950.jpg	1200 x 1920	105px	8.75%	36px	1.88%	0.1%

License #

RELLIS-3D: A Multi-modal Dataset for Off-Road Robotics is under CC BY-NC-SA 3.0 US license.

Source

Citation #

If you make use of the RELLIS-3D-RGB data, please cite the following reference:

@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}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-rellis-3d-rgb-dataset,
  title = { Visualization Tools for RELLIS-3D: RGB Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/rellis-3d-rgb } },
  url = { https://datasetninja.com/rellis-3d-rgb },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset RELLIS-3D: RGB can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='RELLIS-3D: RGB', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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