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 question

Core 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:

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:

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

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

Kalman Filter Tuning

Common Coordinate Frames

Reference Files

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

Simulation Tools

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.