README.md

---
license: cc-by-4.0
viewer: false
pretty_name: "HERCULES: Multi-Robot Photorealistic Synthetic SLAM Dataset"
language:
- en
tags:
- robotics
- slam
- multi-robot
- collaborative-perception
- lidar
- stereo
- depth
- semantic-segmentation
- synthetic
- unreal-engine
- airsim
task_categories:
- robotics
- depth-estimation
- image-segmentation
---

# HERCULES — Multi-Robot Photorealistic Synthetic SLAM Dataset

*A photorealistic synthetic dataset for multi-robot SLAM and collaborative perception —
2 aerial + 2 ground robots across 4 large-scale environments.*

<p align="center">
  <video src="https://huggingface.co/datasets/GeorgiaTech/HERCULES/resolve/main/assets/hercules_hero.mp4" controls loop width="90%"></video>
</p>

<p align="center">
  <a href="https://lunarlab-gatech.github.io/HERCULES-website/"><b>🌐 Project Website</b></a>
  &nbsp;·&nbsp;
  <a href="https://sites.gatech.edu/lunarlab/"><b>🔬 LunarLab @ Georgia Tech</b></a>
  &nbsp;·&nbsp;
  <a href="https://arxiv.org/abs/2606.22756"><b>📄 Paper (arXiv)</b></a>
</p>

### The four environments
<table>
  <tr>
    <td align="center"><b>Australian Outback — Center</b><br>
      <video src="https://huggingface.co/datasets/GeorgiaTech/HERCULES/resolve/main/assets/center.mp4" controls loop muted autoplay playsinline width="100%"></video></td>
    <td align="center"><b>Australian Outback — Perimeter</b><br>
      <video src="https://huggingface.co/datasets/GeorgiaTech/HERCULES/resolve/main/assets/perimeter.mp4" controls loop muted autoplay playsinline width="100%"></video></td>
  </tr>
  <tr>
    <td align="center"><b>City Block</b><br>
      <video src="https://huggingface.co/datasets/GeorgiaTech/HERCULES/resolve/main/assets/city.mp4" controls loop muted autoplay playsinline width="100%"></video></td>
    <td align="center"><b>Forest</b><br>
      <video src="https://huggingface.co/datasets/GeorgiaTech/HERCULES/resolve/main/assets/forest.mp4" controls loop muted autoplay playsinline width="100%"></video></td>
  </tr>
</table>

HERCULES provides time-synchronized, multi-modal sensor streams from a **team of robots
(2× drone, 2× Husky UGV)** operating together in **four large-scale environments**.
It targets research in **SLAM / LiDAR-inertial & visual-inertial odometry, multi-robot /
collaborative perception, depth estimation, and semantic segmentation.**

The data is **synthetic**, generated by **HERCULES** — a simulation framework built on
**Unreal Engine 5** that extends **AirSim** (Shah et al., 2018) and **Cosys-AirSim**
(Jansen et al., 2023) as a UE5 plugin, using Lumen global illumination and Nanite geometry
for photorealistic rendering. It provides photorealistic imagery alongside **perfect,
noise-free ground truth** for geometry, semantics, and trajectories. All streams share a
common time base (synchronized capture).

---

## Sequences (environments)

| Folder | Environment | Approx. size |
|---|---|---:|
| `Australia Center Sequence/`    | Australian outback — center route     | ~379 GB |
| `Australia Perimeter Sequence/` | Australian outback — perimeter route   | ~427 GB |
| `City Block Sequence/`          | Urban city block                       | ~241 GB |
| `Forest Sequence/`              | Dense forest                           | ~312 GB |

Total ≈ **1.1 TB**. Designed trajectory lengths range **359–945 m** per sequence, with
intra- and inter-robot loop closures.

---

## Sensors

Each robot (2× drone, 2× Husky UGV) carries an identical, synchronously-logged suite:

| Modality | Details | Format · rate |
|---|---|---|
| **RGB** | front camera, 752×480, 90° FOV | `.png` · 20 Hz |
| **Stereo** | left + right, 752×480, **0.11 m baseline** | `.png` · 20 Hz |
| **Depth** | planar metric depth, 752×480 | `.npy` (float32, **metres**) + `.png` viz · 20 Hz |
| **Segmentation** | ground-truth semantic + instance labels | `.png` 752×480 (+ `label_color_map_*.csv`, 320 classes) · 20 Hz |
| **LiDAR** | 16-channel, 200 m range, ~28,800 pts/scan | `.npy` N×3 (x,y,z) float32 metres · 20 Hz |
| **IMU** | linear accel + angular velocity (+ 9-axis variant) | `imu.txt` 200 Hz; `synthetic_imu_9axis_{200,500}Hz.txt` |
| **Pose (GT)** | global world-frame + odometry-frame pose | `pose_world_frame.txt`, `odom.txt` |

Camera/LiDAR mounts, FOV, and the stereo baseline are specified in each sequence's
`data/settings.json`.

---

## Directory structure

All four sequences share the same layout (City Block additionally has a second
`results/openvins_BFeb8/` run):

```
<Sequence>/
├── data/
│   ├── Drone1/  Drone2/  Husky1/  Husky2/          # identical per-robot sensor suite:
│   │   ├── rgb/  rgb_stereo_left/  rgb_stereo_right/   # 752×480 PNG, 20 Hz
│   │   ├── depth/                                  # .npy (float32 metres) + .png viz, 752×480
│   │   ├── seg/                                    # GT segmentation PNG (see label_color_map_*.csv)
│   │   ├── lidar/                                  # .npy N×3 (x,y,z) point clouds, 16-ch, 20 Hz
│   │   ├── imu.txt                                 # IMU @ 200 Hz
│   │   ├── synthetic_imu_9axis_200Hz.txt / _500Hz.txt
│   │   └── pose_world_frame.txt  odom.txt          # ground-truth poses
│   ├── trajectory_information/                     # designed reference (waypoint) trajectories
│   ├── settings.json                              # capture config: sensor intrinsics + extrinsics
│   ├── label_color_map_*.csv                      # semantic class ↔ RGB (320 classes)
│   └── environment.png , UE5*world*.png            # environment reference imagery
└── results/
    ├── LIO-SAM/                                   # baseline LiDAR-inertial odometry output
    └── openvins/                                  # baseline visual-inertial odometry output
```

Filenames encode the capture time in **simulation seconds** (e.g. `lidar/0.050000.npy`
→ t = 0.05 s). Cameras + LiDAR are logged at **20 Hz** (Δt = 0.05 s) and IMU up to
**500 Hz**; all streams share a common time base, so samples align across sensors and robots.

### File formats
- **Poses** (`pose_world_frame.txt`, `odom.txt`): `timestamp  x y z  qw qx qy qz` —
  position in metres, **unit quaternion (w-first)**. `pose_world_frame` is the global world
  frame; `odom` starts at the robot's origin.
- **IMU** (`imu.txt`): `timestamp  aₓ a_y a_z  ωₓ ω_y ω_z` at 200 Hz. The
  `synthetic_imu_9axis_{200,500}Hz.txt` files provide a 9-axis IMU at 200 / 500 Hz.
- **Depth:** `.npy` float32 **planar depth in metres** (with a `.png` for quick viewing).
- **LiDAR:** `.npy` array of **N×3 (x, y, z)** points in metres.
- **Segmentation:** `.png` whose colors map to classes via `label_color_map_*.csv`
  (columns: `Label, ObjectName, SegmentationID, R, G, B`; **320 classes**). Instance IDs are
  consistent across robots for cross-view data association.
- **World axis convention:** the AirSim / Cosys-AirSim native world frame; sensor extrinsics
  (camera/LiDAR mounts, baseline) are in `data/settings.json`.

### `results/` — baseline odometry/SLAM outputs
Per-sequence outputs of the baselines benchmarked in the paper:
`LIO-SAM/` (LiDAR-inertial, Shan et al., 2020) and `openvins/` (visual-inertial,
Geneva et al., 2020). City Block additionally includes an alternate `openvins_BFeb8/` run.

---

## Dataset notes
- **Noise-free ground truth.** No sensor-noise model is applied — IMU, poses, depth, LiDAR,
  and segmentation are exact ground truth. (The simulator *can* inject per-sensor noise and
  latency, but it is off for this release.) Add noise externally if your method requires it.
- Trajectories are designed with HERCULES's **Complementary Coverage** planner; each begins
  with a static + calibration period.
- **Dynamic objects** (pedestrians, traffic, wildlife) are disabled during collection
  **except birds**.

---

## How to download and unpack

To keep the dataset usable on the Hub, each per-robot / per-result folder is stored as a
**`.tar.zst` archive** (raw loose files would exceed the Hub's 10,000-files-per-folder
limit). Small metadata files (`settings.json`, `label_color_map_*.csv`, `*.png`) are stored
uncompressed so you can preview them directly.

**Download (whole dataset or a single sequence):**
```bash
pip install -U "huggingface_hub[hf_xet]"

# everything:
hf download GeorgiaTech/HERCULES --repo-type dataset --local-dir HERCULES

# or just one sequence:
hf download GeorgiaTech/HERCULES --repo-type dataset \
    --include "Forest Sequence/*" --local-dir HERCULES
```

**Unpack to the original tree** (reproduces the exact folder structure, byte-for-byte):
```bash
cd HERCULES
./extract_all.sh          # extracts every .tar.zst in place; safe to re-run
# requires: tar + zstd  (sudo apt install zstd)
```
After extraction you get e.g. `Forest Sequence/data/Drone1/lidar/769.900000.npy`, identical
to the source dataset. The `.tar.zst` files can then be deleted if you wish.

---

## Intended uses
- Multi-robot / collaborative SLAM and pose-graph optimization
- LiDAR-inertial and visual-inertial odometry benchmarking (ground truth provided)
- Depth estimation and semantic/instance segmentation (perfect synthetic labels)
- Heterogeneous UAV–UGV perception; cross-environment / sim-to-real studies

## License
Released under **CC-BY-4.0** — free to use and adapt **with attribution**. This dataset
accompanies a manuscript under review at the *International Journal of Robotics Research
(IJRR)*; please cite the paper below.

## Citation
```bibtex
@misc{garimella2026hercules,
  title         = {HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration},
  author        = {Garimella, Sandilya Sai and Butterfield, Daniel Chase and Wilson, Sean and Gan, Lu},
  year          = {2026},
  eprint        = {2606.22756},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url           = {https://arxiv.org/abs/2606.22756}
}
```

## Contact / maintainers
Sandilya Sai Garimella, Daniel Chase Butterfield, Sean Wilson, and Lu Gan —
[LunarLab](https://sites.gatech.edu/lunarlab/), Georgia Institute of Technology.
Project website: <https://lunarlab-gatech.github.io/HERCULES-website/>.

## Acknowledgements
Built on Unreal Engine 5, AirSim (Shah et al., 2018), and Cosys-AirSim (Jansen et al., 2023).