PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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Visual localization in fused image and laser range data

Summary


Nicholas Carlevaris-Bianco, Anush Mohan, James R. McBride and Ryan M. Eustice, Visual localization in fused image and laser range data. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4378-4385, San Francisco, CA, USA, September 2011.

Abstract

This paper reports on a method for tracking a camera system within an a priori known map constructed from co-registered 3D light detection and ranging (LIDAR) and omnidirectional image data. Our method pre-processes the raw 3D LIDAR and camera data to produce a sparse map that can scale to city-size environments. From the original LIDAR and camera data we extract visual features and identify those that are most robust to varying viewpoint. This allows us to include only the visual features that are most useful for localization in the map. Additionally, we quantize the visual features using a vocabulary tree to further reduce the mapś file size. We then use vision-based localization to track the vehicleś motion through the map. We present results on urban data collected with Ford Motor Companyś autonomous vehicle testbed. In our experiments the map is built using urban data from winter 2009, and localization is performed using data collected in fall 2010 and winter 2011. This demonstrates our algorithmś robustness to temporal changes in the environment.

Bibtex entry

@INPROCEEDINGS { ncarlevaris-2011b,
    AUTHOR = { Nicholas Carlevaris-Bianco and Anush Mohan and James R. McBride and Ryan M. Eustice },
    TITLE = { Visual localization in fused image and laser range data },
    BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
    YEAR = { 2011 },
    MONTH = { September },
    ADDRESS = { San Francisco, CA, USA },
    PAGES = { 4378--4385 },
}