Robust monocular SLAM towards motion disturbance

• 作 者: Wei Liu Nanning Zheng Zejian Yuan Pengju Ren Tao Wang
• 机 构: Institute of Artificial Intelligence and Robotics,Xi’an Jiaotong University,AVIC Xi’an Flight Automa
• 出 处: Chinese Science Bulletin
• 发表日期: 2014年17期
• 关键词: SLAM disturbance camera sudden tracker tracked handle drawback adjust shaking

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.

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. expe riments 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.