Voting-based pose estimation for robotic assembly using a 3D sensor

By Changhyun Choi, Yuichi Taguchi, Oncel Tuzel, Ming-Yu Liu, Srikumar Ramalingam. 2012

174 citations recorded by the source corpus.

We propose a voting-based pose estimation algorithm applicable to 3D sensors, which are fast replacing their 2D counterparts in many robotics, computer vision, and gaming applications. It was recently shown that a pair of oriented 3D points, which are points on the object surface with normals, in a voting framework enables fast and robust pose estimation. Although oriented surface points are discriminative for objects with sufficient curvature changes, they are not compact and discriminative enough for many industrial and real-world objects that are mostly planar. As edges play the key role in 2D registration, depth discontinuities are crucial in 3D. In this paper, we investigate and develop a family of pose estimation algorithms that better exploit this boundary information. In addition to oriented surface points, we use two other primitives: boundary points with directions and boundary line segments. Our experiments show that these carefully chosen primitives encode more information compactly and thereby provide higher accuracy for a wide class of industrial parts and enable faster computation. We demonstrate a practical robotic bin-picking system using the proposed algorithm and a 3D sensor.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Robotics & Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Yin Zhou
2. Oncel Tuzel
3. Andy Zeng
4. Johnny Lee
5. Shuran Song
6. Slobodan Ilic
7. Haowen Deng
8. Tolga Birdal

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Ming-Hsuan Yang
4. Dacheng Tao
5. Oncel Tuzel
6. Jitendra Malik
7. Trevor Darrell
8. Li Fei-Fei

In Control and Systems Engineering

1. Andy Zeng
2. Johnny Lee
3. Oncel Tuzel
4. Shuran Song
5. Yin Zhou
6. Kevin Zakka
7. Tolga Birdal
8. Ming-Yu Liu

In Robotics & Control

1. Andy Zeng
2. Shuran Song
3. Johnny Lee
4. Oncel Tuzel
5. Yin Zhou
6. Ming-Yu Liu
7. Slobodan Ilic
8. Silvio Savarese

Citations in the corpus

• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017, methodology): “Some of the earlier methods for feature representation are [41, 8, 7, 19, 42, 35, 6, 27, 1, 36, 2, 25, 26].”
• Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly (2019, background): “[6] introduces a voting-based algorithm to estimate the poses of component parts for assembly from 3D sensor data.”
• Learning to Rank 3D Features (2014, methodology): “Figure 2 shows an overview of the voting-based pose estimation algorithm using point pair features [7,6].”
• PPFNet: Global Context Aware Local Features for Robust 3D Point Matching (2018, background): “Such wide application makes the local features amenable for use in robotics [6], navigation (SLAM) [36] and scene reconstruction for creation of VR contents and digitalization.”