Real-time tracking and pose estimation for industrial objects using geometric features
By Youngrock Yoon, Guilherme N. DeSouza, A. Kak. 2003
92 citations recorded by the source corpus.
This paper presents a fast tracking algorithm capable of estimating the complete pose (6DOF) of an industrial object by using its circular-shape features. Since the algorithm is part of a real-time visual servoing system designed for assembly of automotive parts on-the-fly, the main constraints in the design of the algorithm were: speed and accuracy. That is: close to frame-rate performance, and error in pose estimation smaller than a few millimeters. The algorithm proposed uses only three model features, and yet it is very accurate and robust. For that reason both constraints were satisfied: the algorithm runs at 60 fps (30 fps for each stereo image) on a PIII-800 MHz computer, and the pose of the object is calculated within an uncertainty of 2.4 mm in translation and 1.5 degree in rotation.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
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
- Andy Zeng
- Johnny Lee
- Li Fei-Fei
- Peter R. Florence
- Alberto Rodriguez
- Josh Tenenbaum
- Anthony Simeonov
- Yilun Du
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Li Fei-Fei
- Jitendra Malik
- Trevor Darrell
- Dacheng Tao
- Xiaogang Wang
- Andrew Zisserman
- Abhinav Kumar Gupta
Citations in the corpus
- Transporter Networks: Rearranging the Visual World for Robotic Manipulation (2020, background): “Considerable research in vision for manipulation has been devoted to object detectors and pose estimators [17, 18, 19, 20, 21].”
- Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation (2021, background): “For known objects, one may rely on pose estimation [36, 46, 49], however, this does not enable”
- VIMA: General Robot Manipulation with Multimodal Prompts (2022, background): “While “pick-and-place” and “wipe” seem simple, they do cover a wide range of tabletop manipulation tasks and are crucial to industrial use cases like warehouse robots (Yoon et al., 2003; Berscheid et al., 2020; Devin et al., 2020; Song et al., 2019).”