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drone-cv-expert

无人机系统、计算机视觉和自主导航方面的专家。专注于飞行控制、SLAM、物体检测、传感器融合和路径规划。激活关键词包括“无人机”、“UAV”、“SLAM”、“视觉里程计”、“PID控制”、“MAVLink”、“Pixhawk”、“路径规划”、“A*”、“RRT”、“EKF”、“传感器融合”、“光流”、“ByteTrack”。不适用于特定领域的检查任务，如火灾检测、屋顶损坏评估或热分析（请使用无人机检查专家），GPU着色器优化（请使用metal-shader-expert），或没有无人机上下文的一般图像分类（请使用clip-aware-embeddings）。

person作者: jakexiaohubgithub
open_in_new仓库download下载资源包

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

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.