Abstract: Aiming at the problems of dynamic environmental changes, a lack of natural environmental characteristics, and the dependence on the number of reflector columns of the traditional trilateral localization algorithm based on reflective columns under the ship segmentation operation environment, an improved positioning strategy by introducing sensors such as Light Detection and Ranging, Inertial Measurement Unit (IMU), and odometer, and fusing multi-source sensor data is proposed. The algorithm fuses trilateral positioning with the iterative closest point (ICP) algorithm for point cloud matching for positioning and introduces a factor graph optimization framework to achieve multi-source data fusion. The experimental platform is built to carry out the localization test, and the static X/Y/heading localization error reaches 12.876 mm, 4.273 mm and 0.0003 rad respectively, and the X/Y/heading localization error under dynamic and complex conditions reaches 33.364 mm, 16.95 mm and 0.0263 rad respectively, which is better than the traditional method in terms of accuracy and robustness. The experimental results show that the proposed localization strategy reduces the static X/Y/heading localization error by more than 5.1% compared with the traditional trilateral localization and Kalman filtering, and reduces the dynamic complex X/Y/heading localization error by more than 14.5%.