Visual field movement detection model based on low-resolution images

In robotic mapping and navigation, simultaneous localisation and mapping (SLAM) is the computational problem of constructing a map of an unknown environment and simultaneously keeping track of an agent's location. The popularity of sweeping robot has made SLAM famous in the last few years, while the recent visual simultaneous localisation and mapping (VSLAM) based on three-dimensional vision makes it more mainstream. To detect direction and distance of visual field movement, we build a visual field movement detection model on low-resolution image. Considering the features of image edge and corners, we mainly utilise the similarity computation of feature points and matching methods in this model to detect the moving direction and distance of vision field. The experimental results show that the proposed detection model is more accurate and efficient in three different conditions, and can precisely figure out where the vision field moves in a short period of time.