Object navigation, which involves localizing and navigating to an object in indoor environments, is a critical component of robotic applications. Conventional training-based methods necessitate extensive annotations, meticulous model design, and prolonged training periods to effectively align the control actions with visual perception. Advancements in visual language models (VLMs) have led to the development of modular approaches that separate perception from actions, utilizing pre-trained knowledge. Under this framework, visual perception can be independently learned without direct control, making exploration prior to task execution an effective strategy. Pre-explored Semantic Map [1, 2, 3, 4, 5], constructed through prior exploration and using visual language models (VLMs), has become a fundamental backbone for robotics tasks.