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EN
In this paper, we present an optimization mechanism for two popular landmark-based mobile robot visual homing algorithms (ALV and HiSS), called vector pre-assigned mechanism (VPM). VPM contains two branches, both of which can promote the homing performance effectively. In addition, to make the landmark distribution satisfy the equal distance assumption, a landmark optimization strategy is proposed based on imaging principle of the panoramic vision. Experiments on both panoramic image database and a real mobile robot have confirmed the effectiveness of the proposed methods.
EN
Visual homing enables mobile robots to move towards a previously visited location solely based on panoramic vision sensors. In this paper, a SIFT-based visual homing approach incorporating machine learning is presented. The proposed approach can reduce the impact of inaccurate landmarks on the performance, and generate more precise home direction with simple model. The effectiveness of the proposed approach is verified on both panoramic image databases and actual mobile robot, experimental results reveal that compared to some traditional visual homing methods, the proposed approach exhibits better homing performance and adaptability in both static and dynamic environments.
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