Realtime State Estimation with Tactile and Visual Sensing for Inserting a Suction-held Object
Name
1803.08014.pdf
Description
Accepted version
Size
1.64 MB
Format
Adobe PDF
Checksum (MD5)
5aa02b4256d5338ada2e7958a95f1460
Author(s)
Yu, Kuan-Ting
Rodriguez Garcia, Alberto
Date Issued
January 2019
Journal
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yu, Kuan-Ting and Alberto Rodriguez. "Realtime State Estimation with Tactile and Visual Sensing for Inserting a Suction-held Object." IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018, Madrid, Spain, Institute of Electrical and Electronics Engineers (IEEE), December 2019. © 2018 IEEE
Version
Author's final manuscript
Abstract
We develop a real-time state estimation system to recover the pose and contact formation of an object relative to its environment. In this paper, we focus on the application of inserting an object picked by a suction cup into a tight space, a key technology for robotic packaging. We propose a framework that fuses tactile and visual sensing. Visual sensing is versatile and non-intrusive, but suffers from occlusions and limited accuracy, especially for tasks involving contact. Tactile sensing is local, but provides accuracy and robustness to occlusions. The proposed algorithm to fuse them is based on iSAM, an on-line estimation technique, which we use to incorporate kinematic measurements from the robot, contact geometry of the object and the container, and visual tracking. In this paper, we generalize previous results in planar settings [1] to a 3D task with more complex contact interactions. A key challenge is that we do not observe contact locations between the suction-held object and the container directly. We propose a data-driven method to infer the contact formation, which is then used in real-time by the state estimator. We demonstrate and evaluate the algorithm in a setup instrumented to provide groundtruth.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2018.8594077
