Suitability analysis based on multi-feature fusion visual saliency model in vision navigation

Matching-area suitability analysis in vision navigation system for unmanned aerial vehicle (UAV) is a very worthy but full of challenges research area. In this paper, a multi-feature fusion based visual saliency model (MFF-VSM) was established by introducing invariant features of speeded-up robust features (SURF) directly into the visual saliency model, based on which the extraction method of suitable matching-areas was proposed. With the integration of cross-scale SURF feature maps in the way we defined, the conspicuity map of SURF channel is obtained. By adding SURF channel into the traditional visual saliency model and fusing multi-feature of SURF, color, intensity and orientation, the MFF-VSM model is proposed. Based on the MFF-VSM, salient locations in sensed map could be obtained and chosen as suitable matching-areas. Simulation results show that the error of image registration with extracted matching-areas based on MFF-VSM meet the demands of vision navigation system. The proposed method may provide new ideas for autonomous navigation of UAV in the future.