Neuromorphic cameras (NCs) provide high dynamic range and microsecond‐level temporal resolution, making them well‐suited for fast robotic perception. However, at high speeds, they produce large event rates that challenge real‐time processing on lightweight embedded platforms. This work introduces an efficient optical flow (OF) approach for NCs, designed for low‐power hardware (Raspberry Pi 5). Our method optimizes a Lucas–Kanade‐based algorithm through parallelized processing (25% speedup) and gyroscopic derotation ( 99% accuracy) and incorporates an adaptive slicing technique (splitting the continuous event stream for further processing) to improve robustness to rapid scene changes. The method is validated on a fixed‐wing uncrewed aerial vehicle (UAV) flying at 15 m/s, generating up to 21 million events per second. Despite high event rate, our approach enables real‐time altitude estimation and autonomous landing in both daylight and low‐light conditions. NC‐based altitude estimation matches the performance of a conventional camera in standard lighting (100 000 lux) and remains effective in low‐light conditions (30 lux) where the conventional camera fails. These results demonstrate the feasibility of fast, event‐based perception on lightweight UAVs using low‐power embedded systems.