Topological maps are more suitable than metric maps for robotic exploration tasks. However, real-time updating of accurate and detail-rich environmental topological maps remains a challenge. This paper presents a topological map updating method based on the Generalized Voronoi Diagram (GVD). First, the newly observed areas are denoised to avoid low-efficiency GVD nodes misleading the topological structure. Subsequently, a multi-granularity hierarchical GVD generation method is designed to control the sampling granularity at both global and local levels. This not only ensures the accuracy of the topological structure but also enhances the ability to capture detail features, reduces the probability of path backtracking, and ensures no overlap between GVDs through the maintenance of a coverage map, thereby improving GVD utilization efficiency. Second, a node clustering method with connectivity constraints and a connectivity method based on a switching mechanism are designed to avoid the generation of unreachable nodes and erroneous nodes caused by obstacle attraction. A special cache structure is used to store all connectivity information, thereby improving exploration efficiency. Finally, to address the issue of frontiers misjudgment caused by obstacles within the scope of GVD units, a frontiers extraction method based on morphological dilation is designed to effectively ensure the reachability of frontiers. On this basis, a lightweight cost function is used to assess and switch to the next viewpoint in real time. This allows the robot to quickly adjust its strategy when signs of path backtracking appear, thereby escaping the predicament and increasing exploration flexibility. And the performance of system for exploration task is verified through comparative tests with SOTA methods.

翻译：拓扑地图相比度量地图更适用于机器人探索任务。然而，实时更新精确且细节丰富的环境拓扑地图仍具挑战性。本文提出一种基于广义Voronoi图（GVD）的拓扑地图更新方法。首先，对新观测区域进行去噪处理，以避免低效GVD节点误导拓扑结构。随后，设计了一种多粒度分层GVD生成方法，在全局和局部层面控制采样粒度。这不仅保证了拓扑结构的准确性，还增强了对细节特征的捕捉能力，降低了路径回溯概率，并通过维护覆盖图确保GVD间无重叠，从而提升GVD利用效率。其次，设计了具有连通性约束的节点聚类方法以及基于切换机制的连通方法，以避免因障碍物吸引而产生不可达节点和错误节点。采用特殊缓存结构存储所有连通信息，从而提升探索效率。最后，针对GVD单元范围内障碍物导致的前沿误判问题，设计了基于形态学膨胀的前沿提取方法，有效保障前沿的可达性。在此基础上，采用轻量级代价函数实时评估并切换至下一视点。这使得机器人在出现路径回溯迹象时能快速调整策略，从而摆脱困境并增强探索灵活性。通过与SOTA方法的对比测试，验证了系统在探索任务中的性能。