## 1 Introduction  
Human-machine interactive navigation refers to the process through which machines and operator cooperate with each other to control the movement of devices and realize interactive navigation [1]. This type of navigation has been widely used in indoor service robots [2; 3], self-driving cars [4; 5], automatic guided vehicles (AGV) [6; 7], and so on [8; 9]. Among them, the most important part between the operator and machines is the map. The map needs to combine continuous spatial environment information with human abstract intentions, and discover the relationships between intentions and real spatial areas; finally, these connections are abstracted into a sequence of events (that is, a topological map) [10]. Many researchers began to study topological maps very early. Nodes are jointly represented by the sector feature of a laser and a proportional invariant feature of vision, which do not depend on any artificial landmark, and the global location of robots in the process of map creation [11]. However, the laser sector feature is required in the intersection area among different channels, and the visual feature is also required to be proportional invariant. Therefore, the application effect will be affected in such large-scale and featureless scenarios as a living room. The final topological map is sparse, and cannot achieve accurate navigation obstacle avoidance [12]. A self-organizing method of hierarchical clustering (Map-TreeMaps) is proposed in [13]. Each unit of the map represents the structured data of the tree, while the treemap method provides a global view of the local hierarchy. This method enhances the generality of the construction of topological maps and solves the problem that some environmental geometric structures are constrained. Similar to the Chow-Liu tree model in [14], as long as the number of searching layers is big enough, various spatial structures can be detected and clustered separately. In addition, the segmentation results can be easily optimized by thresholding the weights of local subgraphs. Both methods abandon the details of the local subgraph, which makes it difficult to achieve accurate navigation obstacle avoidance. An auxiliary graph is used to solve the problem of local subgraph association in [15], which improves the efficiency of segmentation and doesn't solve the problem of subgraph details. Moreover, the above clustering segmentation cannot deal with large-scale and open space, which is not human-friendly for human-machine interactive navigation. For example, in the application of service robots, large living rooms and long corridors cannot be regarded as a region, but should be divided into several regions according to the actual situation [16]. A novel and efficient method for updating Voronoi diagrams was proposed in [17], which only updates those units that are actually affected by the environment, and finally lower the number of visits and computing time. In addition, a skeleton-based Voronoi diagram method is also proposed, which is particularly effective for noise removal. A simultaneous location and mapping algorithm (VorSLAM) based on Voronoi map representation is proposed in [18]. One of the basic features of this algorithm is that the features correspond to the local map one by one, and each feature is associated with a local map defined on the feature. This not only retains the details of the local map, it also alleviates the problem of large scene segmentation by Voronoi partitioning. However, the Voronoi graph is based on the principle of distance or special structure to divide the space, so that the area obtained is \"basically the same size\", and the special structure also limits the applicability of the algorithm. From the research history of topological map construction, a generalized Voronoi map [19] and spectral clustering [20] are the two main methods for topological map construction at present [21]. For example, a lightweight method is proposed to create maps by combining metric maps and topological information in [22].