Toward Object Discovery and Modeling via 3-D Scene Comparison

摘要：

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.

作者: Evan Herbst Peter Henry Xiaofeng Ren Dieter Fox

作者单位: University of Washington,Department of Computer Science & Engineering,Seattle,WA 98195 Intel Labs Seattle,Seattle,WA 98105 University of Washington,Department of Computer Science & Engineering,Seattle,WA 98195 Intel Labs Se

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 2623-2629

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）