Sensor Fusion & Autonomy

01The Problem

Multi-sensor systems are hard. Time-synchronization drift across cameras, LiDAR, and IMU produces fusion errors that cascade into false positives. Copying frame data between sensor drivers and inference engines eats CPU cycles and adds latency.

02Our Approach

1. 01Hardware timestamping: PPS-based synchronization across all sensors to <1µs
2. 02Zero-copy buffer pipeline: camera → ISP → DMA → inference — no CPU copies
3. 03Kalman / complementary filter tuning for IMU + encoder fusion
4. 04ROS2 DDS middleware with real-time QoS policies
5. 05V4L2 multi-planar buffers for efficient multi-camera capture

03Verified Metrics

All measurements on production-grade hardware, oscilloscope verified

To evaluate your Sensor Fusion & Autonomy needs on your own platform, schedule an embedded architecture audit or scope your platform class with the system requirements calculator.