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

At a Glance

Synopsis

Browse Publications by Ryan Eustice and the rest of the PeRL Team.

Browse by year

2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2000

Theses

Visual localization within LIDAR maps for automated urban driving

Summary


Ryan W. Wolcott and Ryan M. Eustice, Visual localization within LIDAR maps for automated urban driving. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 176-183, Chicago, IL, USA, September 2014.

Abstract

This paper reports on the problem of map-based visual localization in urban environments for autonomous vehicles. Self-driving cars have become a reality on roadways and are going to be a consumer product in the near future. One of the most significant road-blocks to autonomous vehicles is the prohibitive cost of the sensor suites necessary for localization. The most common sensor on these platforms, a three-dimensional (3D) light detection and ranging (LIDAR) scanner, generates dense point clouds with measures of surface reflectivity---which other state-of-the-art localization methods have shown are capable of centimeter-level accuracy. Alternatively, we seek to obtain comparable localization accuracy with significantly cheaper, commodity cameras. We propose to localize a single monocular camera within a 3D prior ground-map, generated by a survey vehicle equipped with 3D LIDAR scanners. To do so, we exploit a graphics processing unit to generate several synthetic views of our belief environment. We then seek to maximize the normalized mutual information between our real camera measurements and these synthetic views. Results are shown for two different datasets, a 3.0 km and a 1.5 km trajectory, where we also compare against the state-of-the-art in LIDAR map-based localization.

Bibtex entry

@INPROCEEDINGS { rwolcott-2014a,
    AUTHOR = { Ryan W. Wolcott and Ryan M. Eustice },
    TITLE = { Visual localization within {LIDAR} maps for automated urban driving },
    BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
    YEAR = { 2014 },
    MONTH = { September },
    ADDRESS = { Chicago, IL, USA },
    PAGES = { 176--183 },
}

Downloads

  1. Overview Video