When adopting a single-layer strategy of environment exploration, the autonomous map building of a mobile robot in an unknown indoor environment is inefficient.To solve the problem, a composite detector of frontier points is proposed, which relies on the synchronous operation of the global detector and the local detector, as well as the cascading of the filters.This paper adopts a multi-layer strategy of fast environment exploration, which is composed of global detection and local detection, and combines the filtering of candidate frontier points with the comprehensive evaluation of value of the frontier points, so as to determine the most valuable frontier point in the exploration.The data processing method which fuses the dimension reduction of point cloud data with the controller of environment exploration is given.An experimental system of the autonomous environment exploration and map building of a mobile robot has been developed, and the experimental results show that, based on the proposed method, the robot can track the most valuable frontier point in the autonomous environment exploration and SLAM (Simultaneous Localization and Mapping), which improves the efficiency of environment exploration and map building.