Abstract: The 1-point random sample consensus(RANSAC) algorithm is a data association algorithm with high accuracy and low compaction cost.However,it fails when angular velocities around multiple axes of the camera change quickly,and causes the risk of filter divergence when applied to the monocular visual simultaneous localization and mapping(SLAM) of unmanned helicopter.For this problem,2-point RANSAC algorithm is proposed,which incorporates a priori information from the EKF(extended Kalman filter) motion model,and uses RANSAC,in which only 2 matched points are used for sampling,to remove the outliers.Monocular visual SLAM based on 2-point RANSAC algorithm is performed on a mini unmanned helicopter(MUH) platform.The field-experiment results show that 2-point RANSAC algorithm works reliably, and the SLAM's pose estimation is precise enough for autonomous flight.