A Fast Global Adaptive Solution to Low-light Images Enhancement in Visual SLAM

Simultaneous Localization and Mapping (SLAM) is considered as an essential basis for autonomous robots and vehicles. Visual SLAM applies visual sensors to collect information and estimate the motion of the sensor in an unknown environment. However, it relies heavily on feature matching within images, so that the quality of the images has a great effect on the efficiency. In a low light intensity environment, the performance of motion estimation declines seriously. This paper presents a global adaptive solution to compensate the light condition and applies this solution to motion estimation in ORB-SLAM as an optimization module. Key of the solution is to obtain global adaptive brightness gain for image intensifiers. The performance of the image intensifier is also evaluated in this paper as well as the proof of higher estimation accuracy. The result of the strengthened ORB- SLAM on the TUM benchmark shows that there is a significant performance boost especially in low luminance environment.