Sensor Fusion at the Edge: IMU and Camera Integration for Embedded AI

What happens when your camera loses every landmark in a single frame, but your drone still has six meters of warehouse corridor to clear? Single-sensor perception is fragile. A flickering fluorescent, a matte floor, a white wall-any one of them can blind a camera and crash a visual odometry pipeline. The textbooks teach fusion algorithms for workstations with CUDA cores and Vicon ceilings, not for a 480 MHz Cortex-M7 with 512 KB of RAM and a six-hour battery. That gap between theory and silicon is where products fail, budgets burn, and shipping deadlines die. This book closes that gap with focused, end-to-end guidance on inertial-visual fusion for embedded AI systems. - Why a low-cost MEMS IMU can save an entire vision pipeline when the lights go out-and how to synchronize 100 Hz inertial interrupts with 30 fps rolling-shutter frames when your RTOS has only three DMA channels - How to keep a quaternion EKF from silently diverging after forty minutes because you ignored gyroscope temperature-dependent bias drift - The real reason rolling-shutter cameras break under fast motion, and the calibration techniques that fix it without floating-point luxury - From INT8 quantization on sub-watt NPUs to factor-graph optimization on a Jetson Nano: deployment patterns that actually fit inside real thermal and power envelopes - Field-hardened failure analysis: IMU saturation during hard turns, thermal drift in direct sunlight, camera blindness against white walls, and the cascading errors when one sensor lies and the other believes it If you build robots, AR headsets, ADAS systems, or industrial automation that must perceive and move in the real world, this is the technical manual that bridges the gap between research papers and shipping silicon. Stop debugging sensor failures in production. Start building systems that trust neither sensor alone.

Pagina's: 361, Paperback, Independently published