---
license: mit
task_categories:
- object-detection
- robotics
language:
- en
tags:
- Robotics,
- AI,
pretty_name: FAFO
size_categories:
- 10K<n<100K
---
# FAFO Dataset

The FAFO dataset is designed for universal robotics software development. It includes:
- Sensor data: LiDAR scans, GPS coordinates, and IMU readings.
- Image data: Infrared and camera images for object detection and navigation.
- 3D data: Point cloud files for SLAM and mapping.
- Task data: Pre-labeled tasks for robotic arm operations.

## Dataset Overview

### Sensor Data
- **LiDAR Data**: Point cloud scans with timestamps, ranges, intensities, and angles
- **GPS Data**: Precise location data including latitude, longitude, and altitude
- **IMU Data**: Acceleration, angular velocity, and orientation readings

### Image Data
- RGB camera feeds
- Infrared images
- Object detection datasets with bounding box annotations

### 3D Data
- Point cloud maps for SLAM
- 3D environment scans
- Occupancy grid maps

### Task Data
- Pick-and-place task definitions
- Navigation paths
- Robot arm trajectories
- Task annotations and metadata

## Dataset Structure
- `sensor_data/`: Contains JSON files for LiDAR, GPS, and IMU readings.
- `image_data/`: JPEG images for object detection and segmentation.
- `3d_data/`: PCD files for 3D point clouds.
- `task_data/`: JSON files for robotic tasks.

## Usage
This dataset is designed for AI model training, sensor calibration, and robotic task automation.

### Loading the Dataset
```python
from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("GotThatData/fafo")

# Load specific splits
train_dataset = load_dataset("GotThatData/fafo", split="train")
val_dataset = load_dataset("GotThatData/fafo", split="validation")
test_dataset = load_dataset("GotThatData/fafo", split="test")
```

### Example Usage
```python
# Access sensor data
lidar_scan = dataset['train'][0]['sensor_data']['lidar']
gps_reading = dataset['train'][0]['sensor_data']['gps']
imu_data = dataset['train'][0]['sensor_data']['imu']

# Access image data
image_path = dataset['train'][0]['image_data']

# Access 3D data
point_cloud = dataset['train'][0]['3d_data']

# Access task data
task = dataset['train'][0]['task_data']
```

## Data Format

### Sensor Data
```json
{
    "lidar": {
        "timestamp": 1640995200.0,
        "ranges": [1.2, 2.3, 3.4],
        "intensities": [0.5, 0.6, 0.7],
        "angles": [0.0, 0.1, 0.2]
    },
    "gps": {
        "timestamp": 1640995200.0,
        "latitude": 37.7749,
        "longitude": -122.4194,
        "altitude": 0.0
    },
    "imu": {
        "timestamp": 1640995200.0,
        "acceleration": [0.0, 0.0, 9.81],
        "angular_velocity": [0.0, 0.0, 0.0],
        "orientation": [0.0, 0.0, 0.0, 1.0]
    }
}
```

### Task Data
```json
{
    "task_type": "pick_and_place",
    "parameters": {
        "position": [0.5, 0.3, 0.2],
        "orientation": [0.0, 0.0, 0.0, 1.0],
        "gripper_state": "open"
    },
    "annotations": {
        "object_class": "cube",
        "bounding_box": [0.1, 0.1, 0.2, 0.2],
        "confidence": 0.95
    }
}
```

## Dataset Statistics
- Total samples: [Number of samples]
- Train/Val/Test split: 60%/20%/20%
- Data types:
  - Sensor readings: [Number of readings]
  - Images: [Number of images]
  - 3D scans: [Number of scans]
  - Task definitions: [Number of tasks]

## License
MIT License

## Citation
```bibtex
@inproceedings{fafo2024,
    title={FAFO Dataset},
    author={GotThatData},
    year={2024}
}
```