Abstract: To address the challenges of complex dynamic environments in sheep farms, difficult positioning for inspection robots, and low navigation accuracy, this study developed a high-precision 3D laser positioning and autonomous navigation system tailored for real-world sheep farm operations. First, to perceive the sheep farm environment, a fusion approach combining 3D LiDAR and an IMU (Inertial Measurement Unit) was employed. A tightly coupled radar-inertial navigation and mapping (RNAM) algorithm was used to construct the navigation map. Second, employing a viewpoint visibility approach, preliminary filtering of dynamic point clouds is performed. Integrating the concept of ERASOR (Egocentric ratio of pSeudo occupancy-based dynamic object removal), an enhanced dynamic point detection method combining height and distance features is proposed to further filter out interfering dynamic point clouds. Subsequently, precise local positioning is achieved using an ESEKF based on laser odometry and IMU. Stable global positioning is realized through an enhanced adaptive Monte Carlo algorithm integrating NDT-ICP (Normal distribution transform-iterative closest point) to achieve stable global localization. Finally, a path planning method combining the A* algorithm with the Timed-elastic-band (TEB) algorithm is constructed. Experimental results demonstrate that compared to traditional SLAM (Simultaneous Localization and Mapping) algorithms without dynamic point cloud filtering, the proposed filtering algorithm significantly improves robotic positioning accuracy. The average lateral deviation improvement rate reaches 35.2%, the longitudinal deviation improvement rate reaches 28.7%, and overall positioning accuracy increases by 31.8%. When operating at speeds of 0.3–0.5 m/s, the robot exhibits an average heading deviation below 2.4° with a standard deviation under 3.2°, while both lateral and longitudinal deviations maintain average values below 3.5 cm and standard deviations below 2.9 cm. Among the three motion modes—forward, backward, and line-changing—forward mode demonstrated the highest accuracy, while backward and line-changing modes showed slightly reduced precision. Nevertheless, all modes met the autonomous navigation requirements for agricultural robots. The proposed 3D laser positioning and navigation method overcomes the challenges posed by complex dynamic environments in sheep farms, enabling high-precision map construction, positioning, and navigation. This ensures the autonomous operational capability of mobile robots within sheep farm settings, laying a foundation for the application of autonomous mobile platforms in complex agricultural environments.