ICRA 2011 Paper Abstract


Paper WeA104.5

Herbst, Evan (University of Washington), Henry, Peter (University of Washington), Ren, Xiaofeng (Intel Labs Seattle), Fox, Dieter (University of Washington)

Toward Object Discovery and Modeling via 3-D Scene Comparison

Scheduled for presentation during the Regular Sessions "Range Sensing I" (WeA104), Wednesday, May 11, 2011, 09:20−09:35, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Sensor Fusion, Range Sensing, Mapping


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.



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