Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain.
Sensors (Basel). 2018 Aug 15;18(8):2678. doi: 10.3390/s18082678.
Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method.
自由形状物体的姿态估计是灵活可靠的高度复杂自主系统的关键任务。最近，基于范围和 RGB-D 数据的方法已经显示出有希望的结果，具有相对较高的识别率和快速的运行时间。在这方面，本文提出了一种基于点对特征投票方法的刚性物体 6D 姿态估计的基于特征的方法。所提出的解决方案结合了一种新颖的预处理步骤，该步骤考虑了表面信息的判别值，以及用于点对特征的改进匹配方法。此外，还提出了改进的聚类步骤和新颖的基于视图的重评分过程，以及两个场景一致性验证步骤。该方法的性能在一组广泛的、具有真实场景下的杂乱和遮挡的公共可用数据集上与 15 种最先进的解决方案进行了评估。提出的结果表明，所提出的方法在所有数据集上都优于所有测试的最先进的方法，与第二好的方法相比，总体上有 6.6%的相对改进。
