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Efficient 3-D Scene Analysis from Streaming Data

Hanzhang Hu, Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert
Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2013

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Abstract

Rich scene understanding from 3-D point clouds is a challenging task that requires contextual reasoning, which is typically computationally expensive. The task is further complicated when we expect the scene analysis algorithm to also efficiently handle data that is continuously streamed from a sensor on a mobile robot. Hence, we are typically forced to make a choice between 1) using a precise representation of the scene at the cost of speed, or 2) making fast, though inaccurate, approximations at the cost of increased misclassifications. In this work, we demonstrate that we can achieve the best of both worlds by using an efficient and simple representation of the scene in conjunction with recent developments in structured prediction in order to obtain both efficient and state-of-the-art classifications. Furthermore, this efficient scene representation naturally handles streaming data and provides a 300% to 500% speedup over more precise representations.

BibTeX Reference
@conference{Hu-2013-7689,
title = {Efficient 3-D Scene Analysis from Streaming Data},
author = {Hanzhang Hu and Daniel Munoz and J. Andrew (Drew) Bagnell and Martial Hebert},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
sponsor = {ARL-Collaborative Technology Alliance Program},
school = {Robotics Institute , Carnegie Mellon University},
month = {May},
year = {2013},
address = {Pittsburgh, PA},
}
2017-09-13T10:39:26+00:00