> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuronav.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Configuration

> Configure sensors and SLAM algorithms using Python dataclasses with ready-to-use presets

Configure sensors and SLAM algorithms using Python dataclasses. Choose from presets or customize your own.

## Quick Presets

Ready-to-use configurations for common scenarios:

<Tabs>
  <Tab title="Fast Robot">
    For high-speed navigation (drones, fast robots).

    ```python theme={null}
    from neuronav import SensorConfig, SlamConfig

    # Sensor: Low latency
    sensor_config = SensorConfig(
        rgb_width=640,
        rgb_height=480,
        fps=60,  # High FPS
        enable_imu=True
    )

    # SLAM: Fast processing
    slam_config = SlamConfig(
        custom_params={
            "Rtabmap/DetectionRate": "2.0",  # Process less frames
            "Vis/MaxFeatures": "500",  # Less features
            "RGBD/LinearUpdate": "0.2",  # Update less often
            "RGBD/AngularUpdate": "0.2"
        }
    )
    ```
  </Tab>

  <Tab title="3D Mapping">
    For high-quality 3D reconstruction.

    ```python theme={null}
    # Sensor: High resolution
    sensor_config = SensorConfig(
        rgb_width=1920,
        rgb_height=1080,
        depth_width=1280,
        depth_height=720,
        fps=30
    )

    # SLAM: Maximum quality
    slam_config = SlamConfig(
        custom_params={
            "Rtabmap/DetectionRate": "0",  # Process all frames
            "Vis/MaxFeatures": "2000",  # More features
            "Grid/3D": "true",  # 3D occupancy grid
            "Grid/CellSize": "0.01"  # 1cm voxels
        }
    )
    ```
  </Tab>

  <Tab title="Low Power">
    For battery-powered devices.

    ```python theme={null}
    # Sensor: Minimal processing
    sensor_config = SensorConfig(
        rgb_width=640,
        rgb_height=480,
        fps=15,  # Low FPS
        enable_imu=False  # Save power
    )

    # SLAM: Efficient settings
    slam_config = SlamConfig(
        enable_loop_closing=False,  # Save CPU
        custom_params={
            "Rtabmap/MemoryThr": "300",  # Limit memory
            "Vis/MaxFeatures": "300"
        }
    )
    ```
  </Tab>
</Tabs>

## Sensor Configuration

Full `SensorConfig` dataclass with all options:

```python theme={null}
from neuronav import SensorConfig

config = SensorConfig(
    # Device selection
    device_id="123456",     # Camera serial number (optional)

    # Resolution settings
    rgb_width=1280,         # Color image width
    rgb_height=720,         # Color image height
    depth_width=640,        # Depth image width
    depth_height=480,       # Depth image height

    # Performance
    fps=30,                 # Frames per second

    # Features
    enable_imu=True,        # Use IMU if available
    enable_ir=False,        # IR projector/illuminator

    # Advanced parameters
    custom_params={
        "exposure": "auto",  # or specific value in microseconds
        "gain": "16",        # Sensor gain
        "laser_power": "150",  # 0-360 for RealSense
        "temporal_filter": "true",  # Smooth depth over time
        "spatial_filter": "true",   # Smooth depth spatially
        "hole_filling": "true"      # Fill depth holes
    }
)
```

## SLAM Configuration

Full `SlamConfig` dataclass explained:

```python theme={null}
from neuronav import SlamConfig

config = SlamConfig(
    # ROS2 Topics (usually auto-configured)
    rgb_topic="/camera/color/image_raw",
    depth_topic="/camera/depth/image_raw",
    camera_info_topic="/camera/color/camera_info",
    imu_topic="/imu/data",
    odom_topic="/odom",

    # Frame IDs
    robot_base_frame="base_link",
    global_frame="map",
    odom_frame="odom",

    # Core features
    enable_loop_closing=True,     # Detect and close loops
    enable_visualization=False,   # RTAB-Map GUI
    map_publish_frequency_ms=1000,  # Map update rate

    # Docker settings
    use_gpu=False,
    ros_domain_id=0,

    # RTAB-Map parameters
    custom_params={
        # Detection
        "Rtabmap/DetectionRate": "1.0",  # Hz, 0=no limit
        "Rtabmap/MemoryThr": "0",  # Max nodes, 0=unlimited

        # Visual features
        "Vis/FeatureType": "6",  # 6=ORB, 0=SURF, 11=SuperPoint
        "Vis/MaxFeatures": "1000",  # Features per image

        # Loop closure
        "Rtabmap/LoopThr": "0.11",  # Loop closure threshold
        "RGBD/ProximityBySpace": "true",

        # Optimization
        "Optimizer/Strategy": "1",  # 0=TORO, 1=g2o, 2=GTSAM
        "RGBD/OptimizeFromGraphEnd": "false"
    }
)
```

## Usage Examples

### Fast Processing

```python theme={null}
sensor = RealSenseSensor(SensorConfig(
    rgb_width=640,
    rgb_height=480,
    fps=30
))

slam = RTABMapSLAM(SlamConfig(
    custom_params={
        "Rtabmap/DetectionRate": "2.0",
        "Vis/MaxFeatures": "500"
    }
))

run_slam(sensor, slam)
```

### High Accuracy

```python theme={null}
sensor = RealSenseSensor(SensorConfig(
    rgb_width=1920,
    rgb_height=1080,
    fps=30
))

slam = RTABMapSLAM(SlamConfig(
    custom_params={
        "Rtabmap/DetectionRate": "0",
        "Vis/MaxFeatures": "2000"
    }
))

run_slam(sensor, slam)
```

## Docker Deployment

```bash theme={null}
# Build
./docker_build.sh

# Run
./docker_run.sh

# Or use docker-compose
docker-compose up
```

## Visualization

Enable web-based 3D visualization:

```python theme={null}
run_slam(sensor, slam, visualize=True)
# Open http://localhost:8765
```