In the photovoltaic pond environment,the positioning of traditional unmanned ships relies on satellite systems,which are easily blocked by photovoltaic panels, resulting in a significant decline in positioning accuracy. However, the visual synchronous positioning and mapping technology based on point features also has problems such as trajectory drift and error accumulation. Therefore,this paper proposes a binocular vision inertial SLAM system that integrates point - line structural features based on the photovoltaic pond environment,and studies the extraction of centerline features and structural features of the photovoltaic pond. To cope with water surface reflection and illumination changes and improve the quality of line features, this paper improves the ELSED line feature extraction method. To adapt to the environment of photovoltaic ponds,this paper designs elliptic-spherical raster clustering for vanishing points and uses RANSAC to extract structural features on the elliptic- spherical surface. Experimental verification was conducted using the open - source USVInland dataset and the private photovoltaic pond dataset. By comparing the SLAM system in this paper with the ORB - SLAM3 before improvement,the experimental trajectory error decreased significantly by 60% ,and the system tracking frame rate reached 34 frames per second, ensuring the real - time performance of the system. The positioning accuracy met the requirements of unmanned boats in photovoltaic ponds.