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.
Fractal image compression(FIC)technology is an interesting attempt at structure similarity-based image compression.It has been widely applied in many fields such as image encryption,image retrieval,image sharpening,and pattern recognition.However,overlong encoding time is the main difficulty for the application of FIC.In this paper,a new FIC speedup algorithm is proposed with two steps.Firstly,the simplified statistical variable expressions can speed up encoding twice more than the baseline fractal compression(BFC)without loss of image quality corresponding to BFC.Secondly,based on the fact that the affine self-similarity is equivalent to the absolute value of Pearson’s correlation coefficient,a new block classification strategy with flexible classification sets is proposed to speed up encoding further.The experiment results and theoretical analysis show that the proposed scheme achieves high performance in both image quality preservation and encoding efficiency.
