This study presents a localization method based on the improved iterative closest point (ICP) algorithm to solve the localization problems of mobile robots, such as low positioning accuracy and poor real-time positioning, in traditional 2D environments. The algorithm begins by establishing a pose search space, which systematically explored layer-by-layer, transitioning from lower to higher resolutions. To accelerate the search process and eliminate nonoptimal poses, partial point cloud scanning matching was executed synergistically with multipoint cloud density. Adoption of the frame-to-image method by the point cloud matching enabled the effective utilization of historical frame information. Further enhancements in positioning accuracy were achieved through the sparse matrix pose optimization for obtained optimal pose. Tests conducted on the SLAM Benchmark dataset show that the proposed algorithm is considerably more efficient, boasting a 1.8‒4.9 times efficiency gain over the popular Cartographer algorithm, and has less translation error. Real-world tests conducted on Turtlebot2 reveal that the proposed method exhibits substantially fewer positioning errors than Cartographer and Gmapping, showing superior real-time performance. Compared with the traditional adaptive Monte Carlo relocation (AMCL), the proposed method reduces mean translation errors by 0.035 m and mean rotation errors by 0.001 rad, resulting in higher relocation accuracy.