Motion blur caused by high-speed movement often occurs in low-price devices

which is a main factor that affects the accuracy of Simultaneous Localization and Mapping (SLAM). Some approaches dealing with motion blur such as computing blur kernel and blind deconvolution are not suitable for mobile phone

unmanned Areial Vehicle(UAV)

and other platforms with limited processing capacity

which may impact the application of SLAM algorithm. In this study

correspondence was found between coordinate difference and extent of motion blur by exploring the generation of motion blur and the difference of feature coordinates between adjacent images. The average movement of feature points was used to form EBL parameter and represent the blur degree of the frame

which was then combined with frame removal algorithm to continuously remove the big-blur image. The accuracy of localization and mapping under motion blur could be enhanced by adding a small amount of computation. Experiments proved the validity of EBL parameters and the improvement of the accuracy of the SLAM system. Results show that the proposed algorithm could obviously reduce the error of the camera trajectory. For datasets with severe blur

the error could be reduced by 20% under an appropriate size of window.