2D map-building and localization in outdoor environments

Authors:
R. Madhavan;H. F. Durrant-Whyte
Affiliations:
Intelligent Systems Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8230;The Australian Centre for Field Robotics, The University of Sydney, Sydney, NSW 2006, Australia
Venue:
Journal of Robotic Systems
Year:
2005

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Federated-filter-based unmanned ground vehicle localization using 3D range registration with digital elevation model in outdoor environments

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Abstract

Determining the pose (position and orientation) of a vehicle at any time is termed localization and is of paramount importance in achieving reliable and robust autonomous navigation. Knowing the pose it is possible to achieve high level tasks such as path planning. A new map-based algorithm for the localization of vehicles operating in harsh outdoor environments is presented in this article. A map building algorithm using observations from a scanning laser rangefinder is developed for building a polyline map that adequately captures the geometry of the environment. Using this map, the Iterative Closest Point (ICP) algorithm is employed for matching laser range images from the rangefinder to the polyline map. Once correspondences are established, an Extended Kalman Filter (EKF) algorithm provides reliable vehicle state estimates using a nonlinear observation model based on the vertices of the polyline map. Data gathered during field trials in an outdoor environment is used to test the efficiency of the proposed ICP-EKF algorithm in achieving the localization of a four-wheel drive (4WD) vehicle. © 2005 Wiley Periodicals, Inc.