Abstract: To address the issues that current laser simultaneous localization and mapping(SLAM) algorithms are prone to loss of localization in sparse feature scenarios, experience accumulated errors during long-term operation leading to decreased accuracy of localization and map construction, and struggle to maintain the scale consistency of localization and map construction in indoor and outdoor scenarios, a laser SLAM algorithm based on the fusion of IESKF filter and graph optimization is proposed. The algorithm is tested on the KITTI dataset and in real environments, and the localization accuracy and map construction effect of various algorithms are compared and analyzed to verify the effectiveness and superiority of the proposed algorithm.