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Carnegie Mellon Robotics Institute

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Abstract

Autonomous vehicles must be capable of localizing even in GPS denied situations. In this paper, we propose a real-time method to localize a vehicle along a route using visual imagery or range information. Our approach is an implementation of topometric localization, which combines the robustness of topological localization with the geometric accuracy of metric methods. We construct a map by navigating the route using a GPS-equipped vehicle and building a compact database of simple visual and 3D features.We then localize using a Bayesian filter to match sequences of visual or range measurements to the database. The algorithm is reliable across wide environmental changes, including lighting differences, seasonal variations, and occlusions, achieving an average localization accuracy of 1 m over an 8 km route. The method converges correctly even with wrong initial position estimates solving the kidnapped robot problem.

Keywords

topometric, localization, bayes, filter

Notes

Sponsor: Agency for Defense Development

Associated Lab(s) / Group(s): 3D Vision and Intelligent Systems Group

Associated Project(s): Vehicle Localization in Naturally Varying Environments

Text Reference

Hernan Badino, Daniel Huber, and Takeo Kanade, "Real-Time Topometric Localization," International Conference on Robotics and Automation, May, 2012.

BibTeX Reference

@inproceedings{Badino_2012_6979,
   author = "Hernan Badino and Daniel Huber and Takeo Kanade",
   title = "Real-Time Topometric Localization",
   booktitle = "International Conference on Robotics and Automation",
   month = "May",
   year = "2012",
}

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