Tactile SLAM: Real-time inference of shape and pose from planar pushing
Name
2011.07044.pdf
Description
Submitted version
Size
6.77 MB
Format
Adobe PDF
Checksum (MD5)
b38c3047dbfe974a14ba6fbbe54bb371
Author(s)
Suresh, Sudharshan
Bauza, Maria
Yu, Kuan-Ting
Mangelson, Joshua G
Rodriguez, Alberto
Kaess, Michael
Date Issued
2021
Journal
2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Suresh, Sudharshan, Bauza, Maria, Yu, Kuan-Ting, Mangelson, Joshua G, Rodriguez, Alberto et al. 2021. "Tactile SLAM: Real-time inference of shape and pose from planar pushing." 2021 IEEE International Conference on Robotics and Automation (ICRA).
Version
Original manuscript
Abstract
Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.
MIT Department
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/ICRA48506.2021.9562060
