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Proceedings Paper

Improving semantic scene understanding using prior information
Author(s): Ankit Laddha; Martial Hebert
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Paper Abstract

Perception for ground robot mobility requires automatic generation of descriptions of the robot’s surroundings from sensor input (cameras, LADARs, etc.). Effective techniques for scene understanding have been developed, but they are generally purely bottom-up in that they rely entirely on classifying features from the input data based on learned models. In fact, perception systems for ground robots have a lot of information at their disposal from knowledge about the domain and the task. For example, a robot in urban environments might have access to approximate maps that can guide the scene interpretation process. In this paper, we explore practical ways to combine such prior information with state of the art scene understanding approaches.

Paper Details

Date Published: 13 May 2016
PDF: 7 pages
Proc. SPIE 9837, Unmanned Systems Technology XVIII, 98370Q (13 May 2016); doi: 10.1117/12.2231111
Show Author Affiliations
Ankit Laddha, Carnegie Mellon Univ. (United States)
Martial Hebert, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 9837:
Unmanned Systems Technology XVIII
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)

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