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Toward object discovery and modeling via 3-D scene comparison

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4 Author(s)
Herbst, E. ; Dept. of Comput. Sci. & Eng., Univ. of Washington, Seattle, WA, USA ; Henry, P. ; Xiaofeng Ren ; Fox, D.

The performance of indoor robots that stay in a single environment can be enhanced by gathering detailed knowledge of objects that frequently occur in that environment. We use an inexpensive sensor providing dense color and depth, and fuse information from multiple sensing modalities to detect changes between two 3-D maps. We adapt a recent SLAM technique to align maps. A probabilistic model of sensor readings lets us reason about movement of surfaces. Our method handles arbitrary shapes and motions, and is robust to lack of texture. We demonstrate the ability to find whole objects in complex scenes by regularizing over surface patches.

Published in:

Robotics and Automation (ICRA), 2011 IEEE International Conference on

Date of Conference:

9-13 May 2011