中文摘要：

标准扩大 Kalman 基于过滤器同时本地化并且印射(EKF 猛击) 算法有它不能处理的一个缺点突然的运动由运动骚乱引起了。这阻止 SLAM 系统真实应用程序。许多技术被开发了使系统更柔韧到运动骚乱。在这份报纸，我们求婚一柔韧单眼用撞击算法。第一基于模型的系统没能追踪特征的运动，一个 KLT 追踪者将为每个特征被激活。第二， KLT 追踪了特征被用来更新照相机状态。第三，照相机状态之间的差别和预言被用来调整输入运动噪音。最后，我们与新输入运动噪音做标准 EKF 猛击。以便使系统更可靠，一个联合相容性分支和界限算法被用来检查当照相机遇到突然的运动时，孤立点，和一个 IEKF 过滤器被用来使运动评价更光滑。实验在摇晃抓住的一个图象序列上被做手持的照相机，它证明建议方法对大运动骚乱很柔韧。

英文摘要：

The standard extended Kalman filter-based simultaneously localization and mapping （EKF-SLAM） algorithm has a drawback that it could not handle the sudden motion caused by the motion disturbance. This prevents the SLAM system from real applications. Many techniques have been developed to make the system more robust to the motion disturbance. In this paper, we propose a robust monocular SLAM algorithm. First, when the motion model-based system failed to track the features, a KLT tracker will be activated for each feature. Second, the KLT tracked features are used to update the camera states. Third, the difference between the camera states and the predictions is used to adjust the input motion noise. Finally, we do the standard EKF-SLAM with the new input motion noise. In order to make the system more reliable, a joint compatibility branch and bound algorithm are used to check the outliers, and an IEKF filter is used to make the motion estimation smoother when the camera encounters sudden movement. The experiments are done on an image sequence caught by a shaking hand-held camera, which show that the proposed method is very robust to large motion disturbance.