Wheel odometry often does not perform as well as expected on complex surfaces, uneven surfaces, and smooth ground. At the same time, the traditional laser scan matching method does not always correctly correlate the relationship between point clouds, and thus is likely to have abnormal point-to-point correlation, which leads to bad localization accuracy. To solve this problem, a laser scanning matching method based on directional endpoints is proposed. First, we extract straight-line endpoints from the environment as the feature points, and then use the feature matching between the endpoints to obtain the relative pose relationships of the mobile robots in adjacent moments. The directional endpoints are used to eliminate the mismatched feature points to further improve the matching accuracy. Hence, the iterative closest point method is used to further optimize the matching results of the directional endpoints to obtain a better localization result of the mobile robot. The experiment results show that the method achieves an average localization error and an average angle error of 0.12 m and 1.18°, respectively, in an indoor environment of 7 m×7 m, which is superior in accuracy compared with the traditional laser scan matching algorithm.