机器人2012，Vol.34Issue(1)：65-71,7.DOI:10.3724/SP.J.1218.2012.00065
基于2点RANSAC的无人直升机单目视觉SLAM
Monocular Visual SLAM of Unmanned Helicopter Based on 2-point RANSAC
摘要
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.关键词
无人直升机/单目视觉/同步定位与地图构建/数据关联/2点随机抽样一致性Key words
unmanned helicopter/ monocular vision/ simultaneous localization and mapping (SLAM)/ data association/ 2-point random sample consensus (RANSAC)分类
信息技术与安全科学引用本文复制引用
徐伟杰,李平,韩波..基于2点RANSAC的无人直升机单目视觉SLAM[J].机器人,2012,34(1):65-71,7.基金项目
国家973计划资助项目(2009CB320603). （2009CB320603）
