Efficient 3-D Scene Analysis from Streaming Data

Hanzhang Hu, Daniel Munoz, J. Andrew (Drew) Bagnell, and Martial Hebert
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.

Notes
Sponsor: ARL-Collaborative Technology Alliance Program
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center
Associated Project(s): CTA Robotics
Number of pages: 8

Text Reference
Hanzhang Hu, Daniel Munoz, J. Andrew (Drew) Bagnell, and Martial Hebert, "Efficient 3-D Scene Analysis from Streaming Data," IEEE International Conference on Robotics and Automation (ICRA), May, 2013.

BibTeX Reference
@inproceedings{Munoz_2013_7397,
   author = "Hanzhang Hu and Daniel Munoz and J. Andrew (Drew) Bagnell and Martial Hebert",
   title = "Efficient 3-D Scene Analysis from Streaming Data",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   month = "May",
   year = "2013",
}