SKYLINE2GPS: Localization in Urban Canyons using Omni-Skylines

被引：42

作者：

Ramalingam, Srikumar ^{[1
]}

Bouaziz, Sofien ^{[2
]}

Sturm, Peter ^{[3
]}

Brand, Matthew ^{[1
]}

机构：

[1] MERL, Cambridge, MA 02139 USA

[2] Ecole Polytech Fed Lausanne, Lausanne, Switzerland

[3] INRIA Grenoble rhone Alpes, LabJean Kuntzmann, Grenoble, France

来源：

IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010) | 2010年

关键词：

D O I：

10.1109/IROS.2010.5649105

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the problem of geo-localization in GPS challenged urban canyons using only skylines. Our proposed solution takes a sequence of upward facing omnidirectional images and coarse 3D models of cities to compute the geo-trajectory. The camera is oriented upwards to capture images of the immediate skyline, which is generally unique and serves as a fingerprint for a specific location in a city. Our goal is to estimate global position by matching skylines extracted from omni-directional images to skyline segments from coarse 3D city models. Under day-time and clear sky conditions, we propose a sky-segmentation algorithm using graph cuts for estimating the geo-location. In cases where the skyline gets affected by partial fog, night-time and occlusions from trees, we propose a shortest path algorithm that computes the location without prior sky detection. We show compelling experimental results for hundreds of images taken in New York, Boston and Tokyo under various weather and lighting conditions (daytime, foggy dawn and night-time).

引用

页码：3816 / 3823

页数：8