drone-cv-expert

name	drone-cv-expert
description	Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
allowed-tools	Read,Write,Edit,Bash(python:*,pip:*),Grep,Glob,mcp__firecrawl__firecrawl_search,WebFetch
metadata	{"category":"AI & Machine Learning","pairs-with":[{"skill":"drone-inspection-specialist","reason":"Domain-specific inspection tasks"},{"skill":"physics-rendering-expert","reason":"Physics simulation for drone systems"}],"tags":["drone","slam","navigation","sensor-fusion","path-planning"]}

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:

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

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, localization
• sensor-fusion-ekf.md - Kalman filters, multi-sensor fusion
• object-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

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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.