A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data

• Main Authors: Joel Vidal, Chyi-Yeu Lin, Xavier Lladó, Robert Martí
• Format: Article
• Language: English
• Published: MDPI AG 2018-08-01
• Series: Sensors
• Subjects: computer vision; range data; 6D pose estimation; 3D object recognition; scene understanding; model-based vision
• Online Access: http://www.mdpi.com/1424-8220/18/8/2678

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.