Himri Khadidja, Ridao Pere, Gracias Nuno
Underwater Robotics Research Center (CIRS), Computer Vision and Robotics Institute (VICOROB), University of Girona, Parc Científic i Tecnològic UdG C/Pic de Peguera 13, 17003 Girona, Spain.
Sensors (Basel). 2021 Mar 5;21(5):1807. doi: 10.3390/s21051807.
This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available . The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of 51.2%, 68.6% and 90%, respectively, clearly showing the advantages of using the Bayesian estimation (18% increase) and the inclusion of semantic information (21% further increase).
本文提出了一种利用点特征对无颜色点云进行三维物体识别的方法。该方法适用于由管道和连接物体（如阀门、弯头和R型三通连接器）组成的工业海底结构的检查、维护和修理（IMR）等应用场景。识别算法使用存储为点云的物体局部视图数据库，该数据库是可用的。识别流程有5个阶段：（1）平面分割，（2）管道检测，（3）语义物体分割与检测，（4）基于特征的物体识别，以及（5）贝叶斯估计。为了应用贝叶斯估计，提出了一种基于新的间距联合兼容性分支定界（IJCBB）算法的物体跟踪方法。本文研究了取决于以下因素的识别性能：（1）所使用的点特征描述符，（2）是否使用贝叶斯估计，以及（3）是否包含关于物体连接的语义信息。使用包含激光扫描和自主水下航行器（AUV）导航数据的实验数据集对这些方法进行了测试。使用聚类视点特征直方图（CVFH）描述符获得了最佳结果，分别实现了51.2%、68.6%和90%的识别率，清楚地显示了使用贝叶斯估计的优势（提高了18%）和包含语义信息的优势（进一步提高了21%）。
