Capabilities

DroneDev equips commercial drone platforms with combat-grade autonomous capabilities — all running on a phone, fully offline. Every feature below, from neural network-based computer vision to sensor-fused dead reckoning, has been tested on real hardware with INAV and ArduPilot flight controllers.

Navigation

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Dead Reckoning

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When GPS falls — through jamming, spoofing, or terrain masking — DroneDev maintains position using phone camera, AI vision, and IMU sensor fusion at high frequency. An advanced sensor fusion engine combines camera-based positioning with compass, gyroscope, and accelerometer data to deliver continuous navigation through denied environments.

• GPS Monitoring — Continuous health polling with automatic fallback trigger
• Heading Estimation — Fused compass + gyro for reliable orientation when magnetometer is compromised
• Altitude Compensation — Altitude maintained via flight controller telemetry

Visual Homing — Neural Network Wind-Drift Compensation

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When crosswinds push the drone off-course, DroneDev’s deep convolutional neural network (YOLOv8s) automatically detects and locks onto a landmark on the horizon — buildings, towers, trees — and uses it to maintain course. The system compensates for crosswind drift in real time, keeping the drone on target without GPS.

• Neural network selects and tracks landmarks automatically — no manual tagging required
• Real-time heading correction compensates for crosswind drift
• Works in real time on the phone’s NPU — completely offline, no cloud dependency

Search Patterns

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Autonomous search pattern generation for area coverage missions. Four pattern types optimized for different operational needs:

• Grid — Systematic coverage for known-area search
• Spiral — Expanding coverage from a point of interest
• Creeping Line — Linear search along corridors or borders
• Expanding Square — Perimeter security and containment

Patterns execute autonomously with configurable spacing, altitude, and speed parameters.

Computer Vision — AI & Neural Networks

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Object Detection

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YOLOv8s — a deep convolutional neural network — running on-device via TFLite with hardware acceleration on the phone’s dedicated NPU. Detection categories are defined by your training data — customize for personnel, vehicles, structures, or any target class. The neural network performs inference with configurable confidence thresholds.

• Neural network inference on-device — No cloud round-trip latency, no connectivity required
• Persistent tracking — Intelligent matching maintains object identity across frames
• World-coordinate mapping — Pixel detections transformed to real-world coordinates via on-device positioning
• EXIF geotagging — Every capture tagged with coordinates, category, and confidence score

Visual-Inertial Odometry

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High-speed VIO pipeline for continuous pose estimation. Provides the spatial foundation for detection mapping, dead reckoning correction, and visual homing — all without external references.

Autonomy & Patrol

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Autonomous Missions

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Fully autonomous search-and-detect patrols. Configure a search pattern, define detection responses, and launch. The drone executes independently:

• Photo Capture — Document detected objects with geotagged imagery
• Precision Landing — Land at detection coordinates
• Payload Drop — Deploy payload on positive identification
• Track & Follow — Maintain visual lock on moving targets
• Return to Home — Visual homing or dead-reckoned RTH

Sensor Fusion Engine

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Multi-provider architecture aggregates all phone sensors into a unified state estimate:

• IMU — Gyroscope + accelerometer at high frequency
• Compass — Magnetometer with hard/soft iron calibration
• Camera + AI — Visual-inertial odometry and neural network detection
• GPS — When available, fused with dead reckoning for enhanced accuracy

Compatibility

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Zero Connectivity Required

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Every capability runs entirely on the phone. No cloud API calls. No internet dependency. No data exfiltration. DroneDev is designed for operations where connectivity is either unavailable or unacceptable — and that design constraint is treated as a feature, not a limitation.