Visual Topometric Localization - Robotics Institute Carnegie Mellon University

Visual Topometric Localization

Hernan Badino, Daniel Huber, and Takeo Kanade
Conference Paper, Proceedings of IEEE Intelligent Vehicles Symposium (IV '11), pp. 794 - 799, June, 2011

Abstract

One of the fundamental requirements of an autonomous vehicle is the ability to determine its location on a map. Frequently, solutions to this localization problem rely on GPS information or use expensive three dimensional (3D) sensors. In this paper, we describe a method for long-term vehicle localization based on visual features alone. Our approach utilizes a combination of topological and metric mapping, which we call topometric localization, to encode the coarse topology of the route as well as detailed metric information required for accurate localization. A topometric map is created by driving the route once and recording a database of visual features. The vehicle then localizes by matching features to this database at runtime. Since individual feature matches are unreliable, we employ a discrete Bayes filter to estimate the most likely vehicle position using evidence from a sequence of images along the route. We illustrate the approach using an 8.8 km route through an urban and suburban environment. The method achieves an average localization error of 2.7 m over this route, with isolated worst case errors on the order of 10 m.

BibTeX

@conference{Badino-2011-7303,
author = {Hernan Badino and Daniel Huber and Takeo Kanade},
title = {Visual Topometric Localization},
booktitle = {Proceedings of IEEE Intelligent Vehicles Symposium (IV '11)},
year = {2011},
month = {June},
pages = {794 - 799},
}