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The task is complicated by variations in environmental factors which affect the accuracy of localization. To address the challenges of visual localization on variations of illumination, seasons, and viewpoints, this paper proposes a visual localization network based on a gated selection and hybrid receptive field. We utilize a fine-tuned DINOv2 for local feature extraction and leverage a hybrid receptive field to enhance the diversity of visual features. Furthermore, our approach employs spatial gating to dynamically and effectively select and aggregate the advantageous spatial features. 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