Fill dataset card: sensors, formats, frames, rates, noise-free GT, citation

README.md

@@ -49,13 +49,15 @@ task_categories:
 
 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 is intended for research in **SLAM / LiDAR-inertial & visual-inertial odometry,
-multi-robot / collaborative perception, depth estimation, and semantic segmentation.**
+It targets research in **SLAM / LiDAR-inertial & visual-inertial odometry, multi-robot /
+collaborative perception, depth estimation, and semantic segmentation.**
 
-The data is **synthetic**, generated in **Unreal Engine 5** with **AirSim (Cosys-AirSim)**,
-giving photorealistic imagery together with perfect ground-truth geometry, semantics, and
-trajectories.
-<!-- TODO: confirm the simulator stack and add versions (UE5.x, Cosys-AirSim commit/version). -->
+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).
 
 ---
 
@@ -68,39 +70,88 @@ trajectories.
 | `City Block Sequence/`          | Urban city block                       | ~241 GB |
 | `Forest Sequence/`              | Dense forest                           | ~312 GB |
 
-Total ≈ **1.1 TB** across the four sequences.
+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
 
-Each sequence has the same layout (City Block has no `trajectory_information` and an extra
-`results/openvins_BFeb8`):
+All four sequences share the same layout (City Block additionally has a second
+`results/openvins_BFeb8/` run):
 
 ```
 <Sequence>/
 ├── data/
-│   ├── Drone1/  Drone2/  Husky1/  Husky2/      # per-robot sensor streams
-│   │   ├── rgb/                                # RGB camera frames           TODO: res, fps
-│   │   ├── rgb_stereo_left/  rgb_stereo_right/ # stereo pair                 TODO: baseline, res, fps
-│   │   ├── depth/                              # depth maps                  TODO: format, units (m?), encoding
-│   │   ├── seg/                                # semantic segmentation       (see label_color_map_*.csv)
-│   │   └── lidar/                              # LiDAR scans                 TODO: model, format (.bin/.pcd/.ply), rate, channels
-│   ├── trajectory_information/                 # ground-truth trajectories   TODO: format & frame
-│   ├── settings.json                          # AirSim/sim capture settings
-│   ├── label_color_map_*.csv                  # semantic class ↔ RGB color map
-│   └── environment.png , UE5*world*.png        # environment reference imagery
+│   ├── 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/                               # LiDAR-inertial odometry output
-    └── openvins/                              # visual-inertial odometry output
+    ├── LIO-SAM/                                   # baseline LiDAR-inertial odometry output
+    └── openvins/                                  # baseline visual-inertial odometry output
 ```
 
-> **Filenames** encode timestamps (e.g. `lidar/769.900000.png`).
-> <!-- TODO: state the timestamp convention (seconds since start? unix?) and how streams are synchronized across robots. -->
+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.
 
-### Coordinate frames & conventions
-<!-- TODO (important for usability): world frame, per-robot body frame, camera/LiDAR
-     extrinsics, axis convention (e.g. ENU / NED — AirSim is NED by default), and units. -->
+---
+
+## 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**.
 
 ---
 
@@ -129,7 +180,7 @@ 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.png`, identical
+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.
 
 ---
@@ -137,33 +188,29 @@ 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 segmentation (perfect synthetic labels)
-- Cross-environment / sim-to-real and domain-adaptation studies
-
-## Limitations
-- **Synthetic** data — photorealistic but not a substitute for real-world sensor noise
-  characteristics. <!-- TODO: note any sensor-noise models applied, if any. -->
-
----
+- 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 (see Citation) once it is available.
+(IJRR)*; please cite the paper below.
 
 ## Citation
-<!-- TODO: add the paper / BibTeX once available. -->
 ```bibtex
-@misc{hercules_dataset,
-  title  = {HERCULES: A Multi-Robot Photorealistic Synthetic SLAM Dataset},
-  author = {TODO},
-  year   = {TODO},
-  url    = {https://huggingface.co/datasets/GeorgiaTech/HERCULES}
+@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
-**TODO** — name(s), lab (Georgia Tech), and contact email.
+Sandilya Sai Garimella, Daniel Chase Butterfield, Sean Wilson, and Lu Gan — Georgia Institute of Technology.
 
 ## Acknowledgements
-Generated with Unreal Engine 5 and AirSim (Cosys-AirSim). <!-- TODO: funding / grants. -->
+Built on Unreal Engine 5, AirSim (Shah et al., 2018), and Cosys-AirSim (Jansen et al., 2023).