Abstract: This study addresses the challenge of achieving accurate positioning for survey vessels in GNSS-denied environments such as urban waterways,under bridges,and beneath high-pile wharfs.A novel positioning algorithm is proposed that fuses LiDAR with a low-cost MEMS-IMU,aiming to provide relatively high precision under constraints of low computational cost and inexpensive hardware.The algorithm corrects motion distortion in LiDAR point clouds using MEMS-IMU data.It then employs the NDT for point cloud matching to establish a laser odometry function,with KD-tree enhancement for accelerated point cloud search.Finally,the laser odometry is integrated with the MEMS-IMU-derived odometry within a particle filter for fused positioning in these obscured aquatic scenarios.In indoor simulated experiments,the proposed algorithm achieves an average final-position error of 0.20 m,representing a twofold improvement in accuracy over the standalone NDT algorithm and a tenfold improvement over the ICP algorithm.Outdoor experiments conduct under a bridge further demonstrated that the mapping accuracy of the proposed method is significantly superior to both ICP and NDT algorithms.The proposed method achieves relatively high positioning accuracy (better than 0.2 m in both indoor and outdoor tests)at a low cost,demonstrating good potential for real-time positioning of survey vessels or vehicles in GNSS-deprived environments.

Key words: fusion positioning, LiDAR, MEMS_IMU, LiDAR odometry, particle filter