The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We address the problem of understanding scenes from 3-D laser scans via per-point assignment of semantic labels. In order to mitigate the difficulties of using a graphical model for modeling the contextual relationships among the 3-D points, we instead propose a multi-stage inference procedure to capture these relationships. More specifically, we train this procedure to use point cloud statistics and learn relational information (e.g., tree-trunks are below vegetation) over fine (point-wise) and coarse (region-wise) scales. We evaluate our approach on three different datasets, that were obtained from different sensors, and demonstrate improved performance.

Notes

Sponsor: ARL-Collaborative Technology Alliance Program, QinetiQ North America Robotics Fellowship

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Lab(s) / Group(s): Vision and Mobile Robotics Lab

Associated Project(s): CTA Robotics

Number of pages: 8

Text Reference

Xuehan Xiong, Daniel Munoz, J. Andrew (Drew) Bagnell, and Martial Hebert, "3-D Scene Analysis via Sequenced Predictions over Points and Regions," IEEE International Conference on Robotics and Automation (ICRA), May, 2011.

BibTeX Reference

@inproceedings{Xiong_2011_6787,
   author = "Xuehan Xiong and Daniel Munoz and J. Andrew (Drew) Bagnell and Martial Hebert",
   title = "3-D Scene Analysis via Sequenced Predictions over Points and Regions",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   month = "May",
   year = "2011",
}

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