Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2013

Abstract

We describe an architecture to provide online semantic labeling capabilities to field robots operating in urban environments. At the core of our system is the stacked hierarchical classifier developed by Munoz et al.,1 which classifies regions in monocular color images using models derived from hand labeled training data. The classifier is trained to identify buildings, several kinds of hard surfaces, grass, trees, and sky. When taking this algorithm into the real world, practical concerns with difficult and varying lighting conditions require careful control of the imaging process. First, camera exposure is controlled by software, examining all of the image’s pixels, to compensate for the poorly performing, simplistic algorithm used on the camera. Second, by merging multiple images taken with different exposure times, we are able to synthesize images with higher dynamic range than the ones produced by the sensor itself. The sensor’s limited dynamic range makes it difficult to, at the same time, properly expose areas in shadow along with high albedo surfaces that are directly illuminated by the sun. Texture is a key feature used by the classifier, and under/over exposed regions lacking texture are a leading cause of misclassifications. The results of the classifier are shared with higher-lev elements operating in the UGV in order to perform tasks such as building identification from a distance and finding traversable surfaces.

Keywords

Semantic labeling, scene understanding, unmanned vehicles, computer vision

Discipline

Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the SPIE Unmanned Systems Technology XV, Baltimore, Maryland, United States, 2013 April 29 - May 3

Volume

8741

ISBN

9780819495327

Identifier

10.1117/12.2015806

Publisher

SPIE

City or Country

Baltimore, Maryland, USA

Additional URL

https://doi.org/10.1117/12.2015806

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