To improve the efficiency of internal inspections in power plants, this paper proposes an inspection scheme based on intelligent robots. Given the complexity of power plant environments, achieving efficient and accurate autonomous mapping by robots in unknown settings is crucial. We designed an active SLAM method using composite exploration points, incorporating plane segmentation and vector synthesis to guide exploration trajectories, thereby reducing map uncertainty from random exploration. The boundary point evaluation function is enhanced by considering boundary length gain to improve exploration efficiency. The method involves using plane segmentation to search for boundary points around the target, with an evaluation function based on movement distance and boundary length to determine the optimal boundary point with the largest exploration range. Composite exploration points are created through vector synthesis of the optimal boundary and target points, guiding the robot for simultaneous mapping and tracking. Real-time positioning and mapping technology is used to construct the current environmental grid map, achieving target point tracking and autonomous mapping through sequential exploration points. By setting new target points, tracking and expanding the mapping range are achieved. The proposed algorithm exhibits a tendency towards exploration, performing depth-first search on the grid map while considering the traction effect of target points, thereby avoiding multiple trajectory overlaps and loops. Experimental results demonstrate that this method achieves target tracking and high-precision mapping in unknown environments with fewer exploration steps and shorter paths.