A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots.

This paper presents a real-time algorithm for acquiring compact three-dimensional 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 multisurface 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.