# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 1 Introduction
[18] proposed a novel autonomous exploration method based on a prior topometric graph, which verifies that prior information could aid the robot in swiftly completing the exploration of unknown environments. Ref. [19] proposed a path planning method based on topology information for 3D reconstruction, in which the multi-view stereo path planning is decomposed into a collection of overlapped viewing optimization problems that can be processed in parallel. In [14], the prior topometric map is employed to improve exploration efficiency and guide the robot to trigger a loop close, improving the localization accuracy of the Simultaneous Localization And Mapping (SLAM) system. Finally, the environmental information collected will be used to refine prior information. Furthermore, some researchers focus on the generation of topological maps. Ref. [20] proposed a framework called \"topomap\" to provide robots with customized maps to simplify robot navigation tasks, transforming the sparse feature-based map from visual SLAM into a three-dimensional topological map. Ref. [21] proposed an efficient and flexible algorithm that generates a trajectory-independent 3D sparse topological skeleton graph captured from the spatial structure of free space. Inspired by the abovementioned research, we select the topological map as the prior information to guide in robot exploration and employ the frontier-based exploration method suitable for exploring large-scale scenes to plan exploration paths. As a form of map representation, the topological map briefly provides relative position and connectivity between critical places in complex scenes, which could guide the robot in planning paths that follow long-term benefits [20]. In practical cases, many methods can easily acquire the skeleton structure of the environment as the topological map [20; 21; 22; 23]. To fully take advantage of the guiding function of prior topological maps, we propose an autonomous exploration method based on topological maps. The proposed method employs a hierarchical path planning framework, integrates frontier information with prior topological maps, and plans the exploration path with long-term benefits. It first plans a global exploration path by solving the constructed Priority Constrained Asymmetric Traveling Salesman Problem (PCATSP). The global exploration path would follow optimal or customized global exploration strategies to guide the robot to cover frontiers but prioritizes exploring scenes outside the prior information, thus preventing the robot from revisiting previously visited areas. Then, the exploration path is refined from the global exploration path by quickly evaluating the rewards and costs of the candidate viewpoint for each frontier. Because of the one-pass exploration process, our method will maintain the frontier at a small number, preventing excessive computational burden on the solver during exploration. The above properties make our method more suitable for autonomous exploration in large-scale scenes, and the contributions of this paper are as follows:  
(1) An autonomous exploration method based on prior topological maps. The robot follows an optimal or customized strategy to explore a given region autonomously but prioritizes exploring scenes outside prior information, preventing the robot from revisiting the explored areas. (2) A path planning method integrates information between frontiers and prior topological maps, which makes the topological map deeply involved in the path planning of robot exploration. (3) A local path planning method, which quickly evaluates the rewards and costs of each candidate viewpoint to optimize the global exploration path, enhances the stability of exploration efficiency.