Carnegie Mellon University
The
Object Modeling and Recognition from Sparse, Noisy Data via Voxel Depth Carving

Matthew Klingensmith, Martin Hermann, and Siddhartha Srinivasa
International Symposium on Experimental Robotics, June, 2014.


Download
  • Adobe portable document format (pdf) (9MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
In this work, we make the case for using volumetric information for shape reconstruction and recognition from noisy depth images for robotic manipulation. We provide an efficient algorithm, Voxel Depth Carving (a variant of Occupancy Grid Mapping) which accomplishes this goal. Real-world experiments with lasers, RGB-D cameras, and simulated sensors in both 2D and 3D verify the effectiveness of our algorithm in comparison to traditional point-cloud based methods.

Notes

Text Reference
Matthew Klingensmith, Martin Hermann, and Siddhartha Srinivasa, "Object Modeling and Recognition from Sparse, Noisy Data via Voxel Depth Carving," International Symposium on Experimental Robotics, June, 2014.

BibTeX Reference
@inproceedings{Klingensmith_2014_7615,
   author = "Matthew Klingensmith and Martin Hermann and Siddhartha Srinivasa",
   title = "Object Modeling and Recognition from Sparse, Noisy Data via Voxel Depth Carving",
   booktitle = "International Symposium on Experimental Robotics",
   month = "June",
   year = "2014",
}