| 面向狭窄场景的鲁棒多视角配准方法 |
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| 引用本文： | 刘飞,黄瀚霖,杨恬,李文博,杨炀.面向狭窄场景的鲁棒多视角配准方法[J].红外与激光工程,2022,51(12):20220114-1-20220114-9. |
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| 作者姓名： | 刘飞 黄瀚霖 杨恬 李文博 杨炀 |
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| 作者单位： | 重庆大学 机械与运载工程学院 机械传动国家重点实验室，重庆 400044 |
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| 基金项目： | 国家自然科学基金(T2222018) |
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| 摘 要： | 多视角点云配准是逆向工程中的关键步骤之一，具有重要的研究意义和工程应用价值。而对于狭窄场景(如口腔或机械结构内部)获取的点云数据，多视角配准算法的精度直接影响重建精度的好坏。为了提升狭窄场景多视角点云配准的速度和鲁棒性，提出一种基于位姿图优化的增量式多视角点云配准方法。首先针对相邻视角的点云，结合迭代最近点法(ICP)和基于特征的配准方法，提出一种多策略融合的成对点云配准算法，用于求解相邻视角点云的配准结果；然后在增量式相邻视角点云配准的基础上，进一步提出一种基于距离约束的回环检测方法，并依据相邻视角点云的配准结果和回环检测的结果构建位姿图；最后采用实时优化策略对位姿图进行优化，消除累计误差，实现鲁棒的多视角配准。实验结果表明，提出的多策略融合配准算法和基于距离约束的回环检测方法是有效的。经典ICP算法和基于FPFH特征的配准算法在实验中存在失效的现象，而提出的多策略融合配准算法并无失效。基于距离筛选的回环检测方法较常规的回环检测方法效率提高。提出的多视角配准算法在配准牙齿模型数据时精度可达到0.0357 mm。为了验证算法的普适性，采用多个狭窄场景下连续采集的模型点云进行验证，结果表明:提出的算法取得了不错的效果，表明该方法是一种有效的狭窄场景多视角配准方法。
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| 关 键 词： | 多视角点云配准 回环检测 位姿优化 迭代最近点算法 机器视觉 |
| 收稿时间： | 2022-02-21 |
Robust multi-view registration method for narrow scenes |
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| Affiliation: | State Key Laboratory of Mechanical Transmissions, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China |
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| Abstract: | Multi-view point cloud registration is one of the key steps in reverse engineering, which has important research significance and engineering application value. As for point cloud data obtained from narrow scenes (such as oral cavity or mechanical structure), the accuracy of the multi-view registration algorithm directly affects the accuracy of the reconstructed results. In order to improve the speed and robustness of multi-view registration for narrow scenes, an incremental multi-view point cloud registration method based on pose optimization is proposed. Firstly, a multi-strategy registration algorithm is proposed based on iterative closest point method (ICP) and feature-based registration method to solve the registration of adjacent point clouds. Then, based on the incremental registration of adjacent point clouds, a loop closure detection method based on distance constraints is proposed, and the pose graph is constructed according to the registration results of adjacent point clouds and loop closure detection results. Finally, the real-time optimization strategy is used to optimize the pose graph to alleviate drift errors and achieve robust multi-view registration. Experimental results show that the proposed multi-strategy registration algorithm and the loop closure detection method with distance constraints are effective. The classical ICP algorithm and the FPFH-based method are invalid in the experiment, but the proposed multi-strategy registration algorithm is valid. The loop closure detection method with distance constraints is more efficient than the conventional loop closure detection method. The multi-view registration algorithm proposed in this paper can achieve accuracy of 0.0357 mm in tooth model data registration. In order to verify the universality of the algorithm, the model point clouds collected continuously in multiple narrow scenes are used for verification. The results show that the proposed algorithm achieves good results, which indicates that the proposed method is an effective multi-view registration method for narrow scenes. |
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