AeroSense Edge: Autonomous Drone Vision
Low-latency obstacle avoidance and crop health canopy telemetry powered by on-device TensorRT models.
SubsidiaryUrekha Research Lab
Client / DomainAgritech & Infrastructure Partner
Turnaround Time6 Weeks Research & Hardware Testing
CategoryEdge AI & Robotics
<18ms
Edge Inference Latency
500+ Acres/Day
Survey Coverage
99.2%
Obstacle Detection
The Challenge
Drones operating in rural or industrial environments frequently suffer from spotty 4G/5G connectivity, rendering cloud-based obstacle detection unusable.
The Engineering Solution
Engineered an edge inference pipeline utilizing quantized YOLO models running on NVIDIA Jetson companion computers with real-time stereo camera depth estimation.
Deliverables Shipped
Quantized TensorRT Object Detection & Depth Estimation Pipeline
MAVLink Telemetry Integration for Automated Waypoint Override
Real-Time Ground Control Video Telemetry Overlay UI
Full Hardware Integration Schematics & Test Runbooks
Verified Production Impact
Reduced latency to sub-18ms, enabling safe autonomous low-altitude flight at 15 m/s without requiring cellular connectivity.
Technologies Utilized
NVIDIA Jetson OrinTensorRTPyTorch / YOLOv10ROS2MAVLinkC++ / Python
“The latency and obstacle precision of the AeroSense edge pipeline allowed our team to fly beyond visual line of sight with complete confidence.”
Flight Operations Lead
Agritech Partner
Next Case StudySynthetic Ad Lab: 40+ Generative Sprint