基于非完整点云法线滤波补偿的散货船舶舱口识别算法

Hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation

自动装船系统是智能化港口建设的重要组成部分,能够大幅降低港口作业成本,提高经济效益。舱口识别作为自动装船任务的首要环节,成功率和识别精度是后续任务顺利进行的重要保障。由于港口激光雷达的数目和角度等问题,采集所得船舶点云数据时常出现缺失;此外船舶舱口附近经常有大量物料堆积,会使采集到的点云数据无法准确表达舱口的几何信息。由于上述港口实际装船作业中时常出现的问题,显著降低了现有算法的识别成功率,对自动装船作业造成了不良影响,因此迫切需要提升在船舶点云中存在物料干扰或舱口数据缺失的情况下的舱口识别成功率。基于船舶结构特征与自动装船过程中采集的点云数据分析,提出了基于非完整点云法线滤波补偿的散货船舶舱口识别算法。在使用港口实际采集点云所制作的数据集上进行了实验验证,识别成功率和识别精度较Miao和Li的舱口识别算法相比均有提升。实验结果表明,所提算法既能对舱口内物料噪声进行滤除,又能对数据缺失部分进行补偿,能够有效提升舱口识别效果。

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.