A Real-Time Expectation Maximization Algorithm for Acquiring Multi-Planar Maps of Indoor Environments with Mobile Robots
Sebastian Thrun, Christian Martin, Yufeng Liu, Dirk Haehnel, Rosemary Emery-Montemerlo, Deepayan Chakrabarti, and Wolfram Burgard
This paper presents a real-time algorithm for acquiring compact 3D maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on real-time pose estimation during mapping, our approach extends the popular expectation maximization algorithm to multi-surface models, and makes it amenable to real-time execution. Maps acquired by our algorithm consist of compact sets of textured polygons that can be visualized interactively. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real-time and in a fully automated fashion.
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