Accurate and high-rate state estimation is crucial for quadrotors due to its central role in decision, planning and control, which is challenging due to unknown disturbances. Disturbances include internal ones caused by model uncertainties such as mass or lift-coefficient mismatch, and external ones caused by unknown wind or striking forces. To address these challenges, we propose a disturbance-resilient and computationally efficient state estimation method, termed fast and robust moving horizon estimation (FR-MHE), and validate it via onboard experiments on a Crazyflie quadrotor with its resource-constrained STM32F405 MCU. Disturbances are first augmented into quadrotor dynamics to enhance generalization and robustness. We then design a lightweight computational framework via a single-iteration gradient descent to overcome the low computational efficiency of MHE, which otherwise cannot meet the controller’s 100 Hz state estimation demand. Extensive experiments across ten scenarios, including hovering under internal disturbances, wind gusts, payload changes, and striking forces, and flying four typical trajectories under wind gusts, changed paddles, and damaged paddles, demonstrate that, while maintaining estimation performance of Robust MHE (RMHE), FR-MHE improves computational efficiency by 55-126 $\mathbf{\times}$ and reaches sub-millisecond, achieving a runtime comparable to robust extended Kalman filter but with better state estimation capability. Videos are at https://superace0916.github.io/.
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