drone-cv-expert

```

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 question

```

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

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 commands

```

5. "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 |

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

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 |

Detailed implementations in references/:

• navigation-algorithms.md - SLAM, path planning, localization
• sensor-fusion-ekf.md - Kalman filters, multi-sensor fusion
• object-detection-tracking.md - YOLO, ByteTrack, optical flow

| 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 |

• 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

• 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.