drone-cv-expert

license	Apache-2.0
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
category	AI & Machine Learning
tags	["drone","computer-vision","aerial","detection","mapping"]
pairs-with	[{"skill":"drone-inspection-specialist","reason":"Domain-specific inspection tasks"},{"skill":"physics-rendering-expert","reason":"Physics simulation for drone systems"}]

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