Carnegie Mellon University
Real-Time Topometric Localization

Hernan Badino, Daniel Huber, and Takeo Kanade
International Conference on Robotics and Automation, May, 2012.

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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.

topometric, localization, bayes, filter

Sponsor: Agency for Defense Development
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
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
   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",