Vision Options
Computer Vision - See the Field
Computer vision enables robots to detect game pieces, track targets, and localize position using cameras. AprilTag targets provide absolute field positioning, while object detection helps with game piece manipulation.
Key Concept: Vision transforms robots from blind machines into field-aware systems that can adapt to dynamic conditions.
Why Vision Matters in FRC
Absolute Positioning
Game Piece Detection
Target Tracking
💡 Vision is Essential for Competitive Play
Modern FRC requires vision for autonomous accuracy and teleop assistance. Top teams use multiple cameras for comprehensive field awareness.
FRC Vision System Options
Limelight
Dedicated vision hardware with integrated processing, LEDs, and NetworkTables interface. Plug-and-play solution optimized for FRC.
✅ Advantages
- Zero code required for basic detection
- Built-in LED ring for consistent lighting
- Hardware-accelerated processing
- Web interface for tuning pipelines
- NetworkTables integration out-of-box
- Proven reliability in competition
⚠️ Limitations
- Higher cost (~$400-500)
- Proprietary hardware and software
- Limited customization vs open source
🎯 Best For
Teams who want reliable, proven vision hardware with minimal setup. Ideal for teams prioritizing ease-of-use over cost.
PhotonVision
Open-source vision software that runs on coprocessors (Raspberry Pi, Orange Pi, etc.). Flexible and cost-effective alternative.
✅ Advantages
- Free and open source
- Works with any USB camera
- Active development and community
- Advanced AprilTag support
- Multi-tag pose estimation
- Lower total cost (~$100-150)
⚠️ Limitations
- Requires coprocessor setup
- More configuration complexity
- Camera and lighting selection matters
- Performance depends on hardware chosen
🎯 Best For
Teams who want cost-effective vision with flexibility and don't mind additional setup complexity. Great for AprilTag localization.
Hardware Setup
🔌 Power & Connectivity
- Power: Connect Limelight to the PDH/PDP (12V). Do not use the VRM unless necessary.
- Ethernet: Connect directly to the radio or use a network switch if you have multiple devices.
- Mounting: Recommendation for mounting is to mount ideally where the cameras can see scoring target tags when you are scoring. Also make sure not to mount at the same height as tags. You want your cameras looking left or right and up or down when looking at the tag.
🏷️ AprilTag Configuration
Ensure your Limelight is up to date and configured for the current game's AprilTag field map.
- Update Limelight OS to the latest version via USB flash drive.
- Download the latest AprilTag Field Map from the Limelight Downloads page.
- Upload the map via the Limelight web interface (Hardware Manager 2.0).
Vision System Comparison
| Feature | Limelight | PhotonVision |
|---|---|---|
| Cost | $400-500 | $100-150 |
| Setup Complexity | Very Easy | Moderate |
| Hardware | Integrated unit | Coprocessor + Camera |
| AprilTag Support | Yes | Yes |
| Custom Pipelines | Web interface | Web interface + Code |
| Community Support | Large, established | Growing, active |
| Best Use Case | Plug-and-play reliability | Cost-effective flexibility |
AprilTag Localization
AprilTags are fiducial markers placed at known locations on the FRC field. Cameras can detect these tags and calculate the robot's absolute field position.
How AprilTags Work
- Tags have unique IDs corresponding to field positions
- Camera detects tag and calculates relative pose
- Robot position computed from known tag location
- Multiple tags improve accuracy through fusion
Benefits for FRC
- Corrects odometry drift automatically
- Enables accurate autonomous navigation
- Works regardless of starting position
- Provides absolute field coordinates
Multi-Camera Setup
Many top teams use multiple cameras: one facing forward for game pieces, and others positioned to always see AprilTags for continuous localization.
Recommended Approach for This Workshop
📷 Using Limelight
For this workshop, we'll use Limelight for its simplicity and reliability. The focus is on integrating vision data into your robot code, not configuring vision hardware.
What You'll Learn
- Reading vision data from NetworkTables
- Integrating AprilTag poses into odometry
- Using vision for target tracking
- Commanding turrets/shooters based on vision
Additional Resources
What's Next?
Up Next: Implementing Vision
You'll integrate Limelight into your swerve drivetrain for AprilTag-based pose estimation and odometry correction.