Mapping, Navigation, and Learning for Off-Road Traversal

Kurt Konolige, Motilal Agrawal, Morten Rufus Blas, Robert C. Bolles, Brian Gerkey, Joan Sola, Aravind Sundaresan

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain.
Original languageEnglish
JournalJournal of Field Robotics
Volume26
Issue number1
Pages (from-to)88-113
ISSN1556-4959
DOIs
Publication statusPublished - 2009

Cite this

Konolige, K., Agrawal, M., Blas, M. R., Bolles, R. C., Gerkey, B., Sola, J., & Sundaresan, A. (2009). Mapping, Navigation, and Learning for Off-Road Traversal. Journal of Field Robotics, 26(1), 88-113. https://doi.org/10.1002/rob.20271