基于可靠最优传输的点云配准方法

Point cloud registration method based on reliable optimal transport

针对现有的一些配准方法在低重叠场景下依然存在精度差、效率低等不足,本文提出了一种基于可靠最优传输的配准方法。首先,通过关键点及其特征信息形成点对,利用采样一致性算法剔除错误点对并完成粗配准,在优化起始位姿的同时分离出初始的可靠点。其次,在求解最优传输进行精配准的过程中,根据传输方案的迭代和更新策略动态调整参与传输计算的可靠点,从而保证了配准过程的可靠性和高效性。为验证本文方法的有效性,选用斯坦福标准图形库和3DMatch数据集中的部分模型作为配准对象,并将本文方法与常用的3类配准方法进行对比。实验证明,本文方法在配准精度上提升了30%以上,运行时间降低了25%以上。面对多类模型和各种缺失情况,本文方法依然能够保持优秀的配准效果。

For some existing registration methods still suffer from poor accuracy and low efficiency in low overlap conditions,a registration method based on reliable optimal transport is proposed.Firstly,the key points and their feature information are used to form point pairs.The sample consensus algorithm is adopted to reject the wrong point pairs and complete the coarse registration.The initial reliable points are identified while optimizing original position.Secondly,in the process of solving the optimal transport for fine registration,the reliable points involved in the transmission are dynamically adjusted according to the iteration of transport plan and update strategy,which guarantees efficiency and reliability of the registration.To verify the effectiveness of the proposed method,some models in the Stanford standard graphics library and 3DMatch dataset are selected as registration objects,and the proposed method is compared with three common types of registration methods.Experiments results prove that the proposed method improves the accuracy by more than 30%and reduces the running time by more than 25%,which can still maintain excellent registration results in the case of several types of models and various missing conditions.