A High-Speed and Computational Cost-Effective 3D Recognition Method With 2D-Edges-Based 4-Points Congruent Set Algorithm

Abstract

With the increasing application of robots in industrial manufacturing, the 3D vision perception is becoming more and more important. However, many researchers prefer to focus on the accuracy of 3D recognition rather than the speed. Faster speed can accelerate industrial production a lot. Meanwhile, the cost is also an important factor in reality. Since these, we propose the 2D-Edge-based 4-points congruent set (4PCS) algorithm. This algorithm utilizes the affine invariance and the projection invariance to find the correspondence between 3D model and 2D depth image, resulting in a significant increase in speed and keeping the accuracy. Its necessary hardware are just a consumer-level CPU and a consumer-level depth camera. On the Siléane Dataset, the average recognition time is 0.5ms per image.