Aiming at the problems that the traditional 2D laser simultaneous localization and mapping (SLAM) map optimization algorithm in indoor environments is not obvious in point cloud feature extraction when positioning and constructing maps, the front-end accuracy is not high and prone to error accumulation, and the back-end is prone to error loopbacks, we propose a 2D laser SLAM algorithm based on the improvement of the map optimization in complex environments. First, covariance analysis is applied to obtain the point cloud plane change factor to adaptively extract the local neighborhood feature points. Second, the inertial measurement unit (IMU) pre-integration is used in the front-end to provide the initial value for scanning matching. Then the relationship between the eigenvalues of the magnitude of the attitude covariance matrix obtained by scanning matching method is analyzed to determine the robot position in degraded environments and to reduce the problem of localization error of scanning matching. Finally, a two-stage filtering method is used in the loopback detection part to introduce the maximum loopback-compatible subset method to choose the correct closed-loop loop, and mileage checking is performed to eliminate the local neighborhood eigenpoints of SLAM. And the mileage check is performed to eliminate the effect generated by the wrong closed loop in SLAM. The results are validated in real scenarios using a differential wheeled automated guided vehicle (AGV), and show that the front-end pose estimation is highly accurate compared to Hector-SLAM and Cartographer algorithms, and the loop constraints are found accurately in large scenarios where loopback detection is required. The relative error is only about 0.21% compared with the real scene. The results of the study have certain theoretical and engineering significance for improving the accuracy of 2D laser SLAM map construction.