An improved VIO front-end method based on inertial prior correction of image grayscale

Aiming at the problems of camera image sequences with excessive or weak illumination and significant changes in illumination intensity due to different lighting scenarios in a closed environment, a visual-inertial odometer (VIO) system based on inertial prior prediction to enhance images was proposed to solve the problem of features tracking loss due to extreme lighting conditions. This image enhancement algorithm performs grayscale gamma correction on the current image by predicting the pixel positions of feature points using inertia and associating the grayscale relationships between the corresponding feature point neighborhoods in the front and rear images. Experimental verification with TUM dataset shows that the proposed algorithm has an average improvement of 17.7% in positioning accuracy compared to VINS-Mono. Experiments in real indoor and outdoor environments also show better performance compared to VINS-Mono.