The operating cost of the port can be greatly reduced and economic benefits can be greatly improved by the automatic ship loading system， which is an important part of the smart port construction. Hatch recognition is the primary link in the automatic ship loading task， and its success rate and recognition accuracy are important guarantees for the smooth progress of subsequent tasks. Collected ship point cloud data is often missing due to issues such as the number and angle of the port lidars. In addition， the geometric information of the hatch cannot be expressed accurately by the collected point cloud data because there is often a large amount of material accumulation near the hatch. The recognition success rate of the existing algorithm is significantly reduced due to the frequent problems in the actual ship loading operation of the port mentioned above， which has a negative impact on the automatic ship loading operation. Therefore， it is urgent to improve the success rate of hatch recognition in the case of material interference or incomplete hatch data in the ship point cloud. A hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation was proposed， by analyzing the ship structural features and point cloud data collected during the automatic ship loading process. Experiments were carried out to verify that the recognition success rate and recognition accuracy are improved compared with Miao’s and Li’s hatch recognition algorithms. The experimental results show that the proposed algorithm can not only filter out the material noise in the hatch， but also compensate for the missing data， which can effectively improve the hatch recognition effect.
