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Abstract

The extraction of multiple coherent structures from point clouds is crucial to the problem of scene modeling. While many statistical methods exist for robust estimation from noisy data, they are inadequate for addressing issues of scale, semi-structured clutter, and large point density variation together with the computational restrictions of autonomous navigation. This paper extends an approach of nonparametric projection-pursuit based regression to compensate for the non-uniform and directional nature of data sampled in outdoor environments. The proposed algorithm is employed for extraction of planar structures and clutter grouping. Results are shown for scene abstraction of 3D range data in large urban scenes.

Notes

Sponsor: DARPA MARS 2020

Grant ID: NBCH1020014

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

Associated Lab(s) / Group(s): 3D Computer Vision Group

Associated Project(s): MARS2020

Number of pages: 8

Text Reference

Ranjith Unnikrishnan and Martial Hebert, "Robust Extraction of Multiple Structures from Non-uniformly Sampled Data," Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '03), October, 2003, pp. 1322-29.

BibTeX Reference

@inproceedings{Unnikrishnan_2003_4589,
   author = "Ranjith Unnikrishnan and Martial Hebert",
   title = "Robust Extraction of Multiple Structures from Non-uniformly Sampled Data",
   booktitle = "Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '03)",
   pages = "1322-29",
   month = "October",
   year = "2003",
   volume = "2",
}

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