In the photovoltaic river crab breeding pond environment, solar panel obstruction significantly reduces the accuracy of the unmanned operation ship's satellite positioning system. To address this, a laser - inertia - based unmanned operation ship positioning method is proposed, considering the pond's unique conditions. This method improves the Hector - Slam positioning process. First, LiDAR point cloud data undergoes preprocessing to filter disturbances and reduce data size, enhancing accuracy. Then, the map continuity method in Hector - Slam is enhanced by using nonlinear fitting to identify obstacle centers, followed by Gaussian blurring to ensure map continuity, creating a smoother reference map for matching. Next, the map - matching process in Hector - Slam is improved by replacing the Gaussian Newton method with gradient descent, yielding more precise results. Finally, a Kalman filter integrates radar and IMU poses, combining position and heading angle information for improved positioning accuracy. Experimental results show the laser inertial fusion positioning method reduces average positioning deviation by 46% compared to the Hector algorithm. Unlike satellite positioning, which fails to meet the accuracy requirements in photovoltaic ponds, our laser - based method ensures precise positioning. It also outperforms visual schemes in accuracy under disturbances and low - light conditions. Moreover, compared to high - cost 3D laser solutions that are impractical for agricultural production, our cost - effective laser method offers significant advantages. Thus, this laser inertial fusion positioning method can replace satellite positioning, effectively meeting practical production needs.