Self-supervised terrain classification based on moving objects using monocular camera

Donghui Song, Chuho Yi, Il Hong Suh, Byung Uk Choi

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

Abstract

For autonomous robots equipped with a camera, terrain classification is essential in finding a safe pathway to a destination. Terrain classification is based on learning, but the amount of data cannot be infinite. This paper presents a self-supervised classification approach to enable a robot to learn the visual appearance of terrain classes in various outdoor environments by observing moving objects, such as humans and vehicles, and to learn about the terrain, based on their paths of movement. We verified the performance of our proposed method experimentally and compared the results with those obtained using supervised classification. The difference in error rates between self-supervised and supervised methods was about 0-11%.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011
Pages527-533
Number of pages7
DOIs
Publication statusPublished - 2011 Dec 1
Event2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 - Phuket, Thailand
Duration: 2011 Dec 72011 Dec 11

Publication series

Name2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011

Other

Other2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011
CountryThailand
CityPhuket
Period11/12/711/12/11

    Fingerprint

Cite this

Song, D., Yi, C., Suh, I. H., & Choi, B. U. (2011). Self-supervised terrain classification based on moving objects using monocular camera. In 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 (pp. 527-533). [6181340] (2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011). https://doi.org/10.1109/ROBIO.2011.6181340