# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 1 Introduction  
Autonomous robot exploration technology requires robots to collect data within a given region and construct corresponding environmental maps. As a critical technology that reveals robotic autonomy, relevant research in robotics has garnered significant attention, driving widespread applications in geological exploration, 3D reconstruction, post-disaster rescue, and other fields. Numerous autonomous exploration methods have been proposed in recent years and are divided into sampling-based and frontier-based categories. Sampling-based methods originated from the NBV (Next Best View) algorithm in the field of 3D reconstruction. RH-NBV (recurrent hybrid neural-based visual) first introduced the NBV algorithm into the autonomous exploration field [1], which consisted of the robot randomly sampling viewpoints in explored free space, constructing a rapidly exploring random tree (RRT), and evaluating the utility of each branch on the RRT. Finally, the robot focuses on the branch with the highest information reward and selects the first node of this branch as the local target. After that, numerous researchers have extended and improved the RH-NBV to meet the requirements of various application scenarios [2; 3; 4; 5; 6]. However, sampling-based autonomous exploration methods have lower exploration efficiency and lead to the robot being trapped. Ref. [7] first introduced the frontier-based exploration method,which groups free voxels adjacent to unknown voxels as frontier clusters and then drives the robot towards these frontier clusters to move to explore unknown areas. Since then, many frontier-based exploration methods have been proposed to meet various application requirements [8; 9; 10]. Ref. [11] proposed to select a viewpoint with minimal speed changes as the next goal within the Field Of View (FOV) of sensors, aiming to maintain the high movement speed of unmanned aerial vehicles (UAVs) and achieve efficient exploration. Fast UAV expLoration (FUEL) proposes the incremental frontier information structure (FIS) to address the problem of high computation of frontier extraction and low decision frequency of the path planner [12]. Based on FIS, UAVs can quickly and incrementally extract environmental information that the planner needs and promptly plan the exploration path. However, most autonomous exploration methods tend to greedily guide the robot to exploration scenes with immediate rewards and neglect some targets with long-term rewards, resulting in lower efficiency in global exploration [12]. Although some methods plan paths from the global exploration standpoint, the robot inevitably overlooks some scenarios during exploration because of the limited perception range of sensors and the unpredictability of unknown environments [12; 13]. To thoroughly explore a given region, the robot must revisit areas containing those missed scenarios, resulting in a waste of resources. Furthermore, when exploring large-scale scenes, the more information the robot collects with exploration, the more the path planner computes, which poses a significant challenge to onboard computers. In order to solve the above problems, the work [14] proposed that supplying robots with prior information about a given region can aid them in making decisions that align with long-term benefits. Ref. [15] proposed a probabilistic information gain map as the prior knowledge to guide exploration. Ref. [16] introduced a general information theory framework to control multiple robots to search and rescue, wherein the prior knowledge of people is modeled to capture target positions and dynamics. Ref. [17] employs hand-drawn sketches as prior information, enabling the robot to explore even when the metric description of the environment is incomplete. As the concision of a topological map, many researchers use them as prior information to guide robots in autonomous exploration. Ref.