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Penn Subterranean Thermal 900

Repository for PST900 RGB-Thermal Calibration, Dataset and Segmentation Network | C++, Python, PyTorch

If you have any questions regarding this dataset, please raise a GitHub issue here or reach out to us at sshreyas@seas.upenn.edu (Shreyas S. Shivakumar) or rodri651@seas.upenn.edu (Neil Rodrigues).

If you use this dataset, please cite our arXiv paper below:

@misc{shivakumar2019pst900,
    title={PST900: RGB-Thermal Calibration, Dataset and Segmentation Network},
    author={Shreyas S. Shivakumar and Neil Rodrigues and Alex Zhou and Ian D. Miller and Vijay Kumar and Camillo J. Taylor},
    year={2019},
    eprint={1909.10980},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

If you wish to cite our overall DARPA Subterranean Challenge autonomy system, please cite:

@misc{miller2019tunnel,
    title={Mine Tunnel Exploration using Multiple Quadrupedal Robots},
    author={Ian D. Miller and Fernando Cladera and Anthony Cowley and Shreyas S. Shivakumar and Elijah S. Lee and Laura Jarin-Lipschitz and Akhilesh Bhat and Neil Rodrigues and Alex Zhou and Avraham Cohen and Adarsh Kulkarni and James Laney and Camillo Jose Taylor and Vijay Kumar},
    year={2019},
    eprint={1909.09662},
    archivePrefix={arXiv},
    primaryClass={cs.RO}
}

Index

1. Dataset
2. RGB-T Calibration
3. Semantic Segmentation
4. Hardware

PST900 Dataset

* our paper mentions 3416 annotated RGB images. Additional images will be provided on request.

PST900 RGB-T

Sensor Head

Example Image

The directory structure for this dataset is as follows:

.
├── test/                                           # Train data split (606 pairs)
|   ├── rgb/                                        # RGB Images 
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png   
|   |   ├── ...
|   ├── thermal/                                    # Thermal Images (FLIR's AGC 8-bit)
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
|   ├── thermal_raw/                                # Thermal Images (RAW 16-bit)
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
|   ├── depth/                                      # Depth image from stereo depth estimation
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
|   ├── labels/                                     # Human annotated per-pixel label
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
├── train/                                          # Test data split (288 pairs)
|   ├── rgb/                                        # RGB Images 
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png   
|   |   ├── ...
|   ├── thermal/                                    # Thermal Images (FLIR's AGC 8-bit)
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
|   ├── thermal_raw/                                # Thermal Images (RAW 16-bit)
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
|   ├── depth/                                      # Depth image from stereo depth estimation
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...
|   ├── labels/                                     # Human annotated per-pixel label
|   |   ├── 31_bag1a_rect_rgb_frame0000000007.png
|   |   ├── ...

PSTRGB

Example Images

The directory structure for this dataset is as follows:

├── rgb/                                        # RGB Images 
|   ├── 01_levine_rgb_1_rdb_bag_100109   
|   ├── ...
├── labels/                                     # Human annotated per-pixel label
|   ├── 01_levine_rgb_1_rdb_bag_100109
|   ├── ...

Toolkit

We provide a basic set of tools for working with this dataset. This tools are mainly focused on how to read and represent the RGB, Thermal, Thermal (16-bit) and Labels.

Basic utilities can be found in the toolkit/utilities.py script.

Basic Utilities

A demo of the utilities are provided in utilities.py; this can be run as a python script. Provide the location to the dataset to the pst900_path variable and run the following command:

python3 utilities.py

1. Data loader

Once you select a split type (either Train or Test, or another custom split), you can use our dataset sample fetch script:

# Example dataset sample loader
rgb, depth, thermal, thermal_raw, label = utils.get_sample(102)

and each modality should look like this:

==== Loading sample ====
Loaded RGB image | H: 720, W: 1280, C: 3, Type: uint8
Loaded Depth image | H: 720, W: 1280, C: 1, Type: uint16
Loaded Thermal image | H: 720, W: 1280, C: 1, Type: uint8
Loaded Thermal Raw image | H: 720, W: 1280, C: 1, Type: uint16
Loaded label image | H: 720, W: 1280, C: 1, Type: uint8

Note: Depth and Thermal Raw (16 bit) must be loaded as 16-bit images.

The RGB and Thermal (8-bit) can be easily viewed and should look like this:

2. Thermal Hole Filling

The following method will perform a simple hole filling in the thermal images. The holes are a result of projecting the information from the Thermal camera onto the RGB camera. The holes are a result of parallax, resulting in a many-to-one mapping of pixels in RGB into the Thermal camera plane.

# Example hole filling for Thermal image
thermal_filled = utils.fill_thermal(thermal)

The output should look like this:

3. Label visualization

The labels are stored as uint8 images, with each pixel representing the class index {0..4}. This class indices can be mapped to colors for easier visualization as follows:

# Example colormapping for Label image
colormapped_label = utils.visualize_label(label)

The resulting image should look like this: