How ileri005/omninav works
This small OpenCV toolkit has three workflows: generate a printable ArUco marker, diagnose detection from a saved image, and track marker pose from a Raspberry Pi camera. The shared domain stages are marker encoding and marker detection; live mapping adds camera-frame ingestion, pose estimation, and visualization. The scripts call OpenCV directly rather than sharing an internal application module. The README is empty, so capabilities and connections are based on the supplied source; generated image files are runtime artifacts, not listed in the repository tree.
Main entry points: live_mapping.py Track live markers · aruco_detect.py Diagnose image · generate_marker.py Generate marker
Components 15
Marker generation
- Generate marker
generate_marker.py - ArUco generation
- Marker image
Image diagnostics
- Diagnose image
aruco_detect.py - Input image
- Diagnostic image
- Detection report
Live mapping
- Track live markers
live_mapping.py - Pose estimation
live_mapping.py - Tracking visualization
live_mapping.py
Other components
- Operator
- ArUco detection
- Pi camera
- Console
- Viewport
Connections 16
- Operator → Generate marker: runs
- Generate marker → ArUco generation: generates marker
- Generate marker → Marker image: writes image
- Operator → Diagnose image: runs
- Marker image → Input image: provides image
- Diagnose image → Input image: loads image
- Diagnose image → ArUco detection: detects markers
- Diagnose image → Diagnostic image: writes overlay
- Diagnose image → Detection report: prints results
- Operator → Track live markers: runs
- Track live markers → Pi camera: captures frames
- Track live markers → ArUco detection: detects markers
- Track live markers → Pose estimation: estimates pose
- Track live markers → Tracking visualization: draws overlays
- Track live markers → Console: prints coordinates
- Track live markers → Viewport: renders frames