Learning the odometry on a small humanoid robot

Quentin Rouxel, Gregoire Passault, Ludovic Hofer, Steve N'Guyen, Olivier Ly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Odometry is an important element for the localization of mobile robots. For humanoid robots, it is very prone to integration errors, due to mechanical complexity, uncertainties and foot/ground contacts. Most of the time, a visual odometry is then used to encompass these problems. In this work we propose a method to compensate for odometry drifting using machine learning on a small size low-cost humanoid without vision. This method is tested on different ground conditions and exhibits a significant improvement in odometry accuracy.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation (ICRA)
Place of PublicationStockholm, Sweden
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1810-1816
Number of pages7
ISBN (Electronic)978-1-4673-8026-3
DOIs
Publication statusPublished - 9 Jun 2016
Event2016 IEEE International Conference on Robotics and Automation - Stockholm, Sweden
Duration: 16 May 201621 May 2016
https://www.icra2016.org/

Conference

Conference2016 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2016
CountrySweden
CityStockholm
Period16/05/1621/05/16
Internet address

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