To fulfill the need for high-precision positioning capabilities of indoor service robots, this article proposes an indoor lightweight mapping method for laser point clouds based on direct optimization. This method fully considers the structural characteristics of indoor environments and the unique advantages of lidar. First, redundant information, such as ground points, is filtered out through point cloud filtering. Then, long point cloud sequences are segmented into multiple fragments. Inside each fragment, the NDT algorithm is used to provide an initial estimation for registration. Subsequently, nonlinear optimization of poses is conducted based on pixel brightness information to construct accurate local maps. Finally, combined with OPENGL′s multi-layer technology, a complete indoor map is assembled. To evaluate the feasibility and performance of the proposed algorithm, a dedicated point cloud processing software is developed and tested in the internal areas of an experimental building. The results show that, under lightweight and low-configuration conditions, the map constructed by this algorithm maintains a high degree of consistency with the currently well-known algorithm LIOSAM. Meanwhile, the relative error of mapping is controlled within 1%, and the average computation time between frames is 95.8 milliseconds. While demonstrating high precision, it also maintains excellent real-time performance, thus exhibiting potential and value for practical applications.