Abstract: Aiming at the problems of low positioning accuracy and robustness, as well as poor precision and stability in mapping for pasture inspection robots, a novel LOM-SLAM algorithm based on LiDAR ranging and mapping technology and improved LOM-SLAM algorithm is proposed. This algorithm is derived from an enhanced LOAM-SLAM algorithm, which integrates laser range finding and surveying technology. LOM-SLAM decomposes SLAM into two separate processes such as motion estimation and map construction. By leveraging the high precision of laser range finding and surveying technology, LOM-SLAM achieves simultaneous robot localization and map building, thereby enhancing the accuracy, robustness, and stability of both positioning and mapping. LOM-SLAM was installed on a designed inspection robot with Mecanum wheel structure for test verification. The results showed that in pose estimation tests, LOM-SLAM significantly outperformed other methods in terms of relative pose error（RPE） and absolute trajectory error（ATE）, with RMSE values of just 7.28 m and 2.23 m, respectively, which were lower than the comparative algorithms. In the positioning and mapping tests, with the inspection robot moving at speeds of 0.2 m/s, 0.5 m/s, and 1 m/s, the positioning errors of LOM-SLAM were only 0.12 m, 1 m, and 1.2 m, respectively, demonstrating better positioning accuracy and robustness compared to the comparative algorithms.