Drone CV Expert
Expert in robotics, drone systems, and computer vision for autonomous aerial platforms.
Decision Tree: When to Use This Skill
User mentions drones or UAVs?
├─ YES → Is it about inspection/detection of specific things (fire, roof damage, thermal)?
│ ├─ YES → Use drone-inspection-specialist
│ └─ NO → Is it about flight control, navigation, or general CV?
│ ├─ YES → Use THIS SKILL (drone-cv-expert)
│ └─ NO → Is it about GPU rendering/shaders?
│ ├─ YES → Use metal-shader-expert
│ └─ NO → Use THIS SKILL as default drone skill
└─ NO → Is it general object detection without drone context?
├─ YES → Use clip-aware-embeddings or other CV skill
└─ NO → Probably not a drone questionCore Competencies
Flight Control & Navigation
- PID Tuning: Position, velocity, attitude control loops
- SLAM: ORB-SLAM, LSD-SLAM, visual-inertial odometry (VIO)
- Path Planning: A*, RRT, RRT*, Dijkstra, potential fields
- Sensor Fusion: EKF, UKF, complementary filters
- GPS-Denied Navigation: AprilTags, visual odometry, LiDAR SLAM
Computer Vision
- Object Detection: YOLO (v5/v8/v10), EfficientDet, SSD
- Tracking: ByteTrack, DeepSORT, SORT, optical flow
- Edge Deployment: TensorRT, ONNX, OpenVINO optimization
- 3D Vision: Stereo depth, point clouds, structure-from-motion
Hardware Integration
- Flight Controllers: Pixhawk, Ardupilot, PX4, DJI
- Protocols: MAVLink, DroneKit, MAVSDK
- Edge Compute: Jetson (Nano/Xavier/Orin), Coral TPU
- Sensors: IMU, GPS, barometer, LiDAR, depth cameras
Anti-Patterns to Avoid
1. "Simulation-Only Syndrome"
Wrong: Testing only in Gazebo/AirSim, then deploying directly to real drone. Right: Simulation → Bench test → Tethered flight → Controlled environment → Field.
2. "EKF Overkill"
Wrong: Using Extended Kalman Filter when complementary filter suffices. Right: Match filter complexity to requirements:
- Complementary filter: Basic stabilization, attitude only
- EKF: Multi-sensor fusion, GPS+IMU+baro
- UKF: Highly nonlinear systems, aggressive maneuvers
3. "Max Resolution Assumption"
Wrong: Processing 4K frames at 30fps expecting real-time performance. Right: Resolution trade-offs by altitude/speed:
| Altitude | Speed | Resolution | FPS | Rationale |
|---|---|---|---|---|
| <30m | Slow | 1920x1080 | 30 | Detail needed |
| 30-100m | Medium | 1280x720 | 30 | Balance |
| >100m | Fast | 640x480 | 60 | Speed priority |
4. "Single-Thread Processing"
Wrong: Sequential detect → track → control in one loop. Right: Pipeline parallelism:
Thread 1: Camera capture (async)
Thread 2: Object detection (GPU)
Thread 3: Tracking + state estimation
Thread 4: Control commands5. "GPS Trust"
Wrong: Assuming GPS is always accurate and available. Right: Multi-source position estimation:
- GPS: 2-5m accuracy outdoor, unavailable indoor
- Visual odometry: 0.1-1% drift, lighting dependent
- AprilTags: cm-level accuracy where deployed
- IMU: Short-term only, drift accumulates
6. "One Model Fits All"
Wrong: Using same YOLO model for all scenarios. Right: Model selection by constraint:
| Constraint | Model | Notes |
|---|---|---|
| Latency critical | YOLOv8n | 6ms inference |
| Balanced | YOLOv8s | 15ms, better accuracy |
| Accuracy first | YOLOv8x | 50ms, highest mAP |
| Edge device | YOLOv8n + TensorRT | 3ms on Jetson |
Problem-Solving Framework
1. Constraint Analysis
- Compute: What hardware? (Jetson Nano = ~5 TOPS, Xavier = 32 TOPS)
- Power: Battery capacity? Flight time impact?
- Latency: Control loop rate? Detection response time?
- Weight: Payload capacity? Center of gravity?
- Environment: Indoor/outdoor? GPS available? Lighting conditions?
2. Algorithm Selection Matrix
| Problem | Classical Approach | Deep Learning | When to Use Each |
|---|---|---|---|
| Feature tracking | KLT optical flow | FlowNet | Classical: Real-time, limited compute. DL: Robust, more compute |
| Object detection | HOG+SVM | YOLO/SSD | Classical: Simple objects, no GPU. DL: Complex, GPU available |
| SLAM | ORB-SLAM | DROID-SLAM | Classical: Mature, debuggable. DL: Better in challenging scenes |
| Path planning | A*, RRT | RL-based | Classical: Known environments. DL: Complex, dynamic |
3. Safety Checklist
- Kill switch tested and accessible
- Geofence configured
- Return-to-home altitude set
- Low battery action defined
- Signal loss action defined
- Propeller guards (if applicable)
- Pre-flight sensor calibration
- Weather conditions checked
Quick Reference Tables
MAVLink Message Types
| Message | Purpose | Frequency |
|---|---|---|
| HEARTBEAT | Connection alive | 1 Hz |
| ATTITUDE | Roll/pitch/yaw | 10-100 Hz |
| LOCAL_POSITION_NED | Position | 10-50 Hz |
| GPS_RAW_INT | Raw GPS | 1-10 Hz |
| SET_POSITION_TARGET | Commands | As needed |
Kalman Filter Tuning
| Matrix | High Values | Low Values |
|---|---|---|
| Q (process noise) | Trust measurements more | Trust model more |
| R (measurement noise) | Trust model more | Trust measurements more |
| P (initial covariance) | Uncertain initial state | Confident initial state |
Common Coordinate Frames
| Frame | Origin | Axes | Use |
|---|---|---|---|
| NED | Takeoff point | North-East-Down | Navigation |
| ENU | Takeoff point | East-North-Up | ROS standard |
| Body | Drone CG | Forward-Right-Down | Control |
| Camera | Lens center | Right-Down-Forward | Vision |
Reference Files
Detailed implementations in references/:
navigation-algorithms.md- SLAM, path planning, localizationsensor-fusion-ekf.md- Kalman filters, multi-sensor fusionobject-detection-tracking.md- YOLO, ByteTrack, optical flow
Simulation Tools
| Tool | Strengths | Weaknesses | Best For |
|---|---|---|---|
| Gazebo | ROS integration, physics | Graphics quality | ROS development |
| AirSim | Photorealistic, CV-focused | Windows-centric | Vision algorithms |
| Webots | Multi-robot, accessible | Less drone-specific | Swarm simulations |
| MATLAB/Simulink | Control design | Not real-time | Controller tuning |
Emerging Technologies (2024-2025)
- Event cameras: 1μs temporal resolution, no motion blur
- Neuromorphic computing: Loihi 2 for ultra-low-power inference
- 4D Radar: Velocity + 3D position, works in all weather
- Swarm autonomy: Decentralized coordination, emergent behavior
- Foundation models: SAM, CLIP for zero-shot detection
Integration Points
- drone-inspection-specialist: Domain-specific detection (fire, damage, thermal)
- metal-shader-expert: GPU-accelerated vision processing, custom shaders
- collage-layout-expert: Report generation, visual composition
Key Principle: In drone systems, reliability trumps performance. A 95% accurate system that never crashes is better than 99% accurate that fails unpredictably. Always have fallbacks.