Visual odometry using commodity optical flow

Authors:
Jason Campbell;Rahul Sukthankar;Illah Nourbakhsh
Affiliations:
Intel Research Pittsburgh, Pittsburgh, PA and Carnegie Mellon University, The Robotics Institute, Pittsburgh, PA;Intel Research Pittsburgh, Pittsburgh, PA and Carnegie Mellon University, The Robotics Institute, Pittsburgh, PA;Carnegie Mellon University, The Robotics Institute, Pittsburgh, PA
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

Citing 3
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Abstract

A wide variety of techniques for visual navigation using robot-mounted cameras have been described over the past several decades, yet adoption of optical flow navigation techniques has been slow. This demo illustrates what visual navigation has to offer: robust hazard detection (including precipices and obstacles), high-accuracy open-loop odometry, and stable closed-loop motion control implemented via an optical flow based visual odometry system. This work is based on 1) open source vision code, 2) common computing hardware, and 3) inexpensive, consumer-quality cameras, and as such should be accessible to many robot builders.