Improving visual SLAM accuracy through deliberate camera oscillations

Abstract

Visual Simultaneous Localization And Mapping, (VSLAM) algorithms exploit the observation of scene naturally-existing distinct features to infer the camera motion and build a map of a static environment. There is an increasing interest towards building efficient VSLAM algorithms mainly from computational perspectives; however, there may be insufficient clues to solve for SLAM parameters efficiently. In this paper, deliberate camera oscillations are superimposed on the camera main motion (robot motion), mostly in a lateral direction to give sufficient physical clues for the solution. Filtering methods exploit correlation to infer the motion parameters, and since oscillation introduces more local changes, it can enhance the estimation by correlation. Simulation results are presented showing the effects of oscillation parameters on visual SLAM performance in different motion scenarios. The results showed significant improvement of accuracy for oscillating camera over the steady camera case, and in several cases errors are reduced to less than half its value. These simulation results can be the basis to design a real experimental system.