Abstract
This paper presents a computational model for landmarks acquisition in vehicle's positioning. Considering the machine vision, visual attention mechanism of biology, feature-integration theory and the demand of navigation, the computational model is divided into two part: pre-attention state and attention state, and the detailed process includes extraction of feature points, generation of local saliency value, generation of integration saliency value and selection of attention points. At the end of the paper, the proposed computational model is realized through the simulation. What's more, the robustness of the attention points and the vehicle's positioning performance when the attention points are used as landmarks are analyzed. Simulation validates that the computational model is availability. Besides, in terms of the performance of the self-attribute and providing positioning reference, the attention points behave well.
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