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

​
Quick Presets

Ready-to-use configurations for common scenarios:
  • Fast Robot
  • 3D Mapping
  • Low Power
For high-speed navigation (drones, fast robots).
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"
    }
)

​
Sensor Configuration

Full SensorConfig dataclass with all options: 
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: 
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

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

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

# Build
./docker_build.sh

# Run
./docker_run.sh

# Or use docker-compose
docker-compose up

​
Visualization

Enable web-based 3D visualization: 
run_slam(sensor, slam, visualize=True)
# Open http://localhost:8765