Robustness Enhancement of optical flow sensors accuracy to surface texture variations using point tracking algorithm

Novel architecture of high precision localization using optical flow sensor (OFS) combined with Iterative Point Tracking Algorithm (IPTA) is proposed. This work focuses on attenuation of OFS' sensitivity dependency on texture of surface over which sensor is moving. The aim for the design of experimental setup is to verify how much a robustness of OFS's sensitivity on various surfaces improves. In this regard, four different surfaces' texture including iron, paper, textile and granite stone is opted. Experimental results indicate that sensor's resolution via IPTA on surfaces of iron, paper, textile and granite stone respectively equal to 382, 460, 528 and 448 CPI. Optimal value of the algorithm parameters is calculated via Genetic Algorithm (GA). We show that IPTA is one of the effective algorithms that can enhance the robustness of OFS' resolution to surface's texture variations.