一种视觉SLAM中运动模糊应对方法

相机高速移动引起的运动模糊在现实环境的中低端设备中经常出现,是影响实时定位与制图(Simultaneous Localization and Mapping SLAM)高精度运行的重要因素.现有的一些应对模糊的方法通常需要较大的计算量,不适用于计算性能有限的移动端、无人机等平台,这在一定程度上影响了SLAM算法的应用.本文通过序列图像分析运动模糊的产生原理,建立相邻图像特征坐标差与模糊尺度之间的量化关系表,利用相邻图像间匹配点的位置关系定量计算E_(BL)参数来表示图像的模糊程度并与帧筛除算法组成E_(BL)-帧筛除算法,在SLAM过程中不断筛除模糊较大的帧来应对运动模糊.在由于运动产生图像模糊的情况下,通过适当增加一些运算量,本文的方法可以提升SLAM系统的定位和建模精度.实验证明了E_(BL)参数对模糊表达的有效性,以及本文算法对SLAM系统精度的提升.结果表明,算法可以明显地降低相机轨迹估计的整体误差,对于模糊影响较严重的数据集,在合适的窗口大小下,用E_(BL)-帧筛除算法剔除部分帧后,通过余下的清晰帧计算得到的相机位姿整体误差下降了20%.

Algorithm for dealing with motion blur in visual SLAM

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 E_BL 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 E_BL 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.