Using EM to Learn 3D Models of Indoor Environments with Mobile Robots

This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a lowcomplexity planar model to 3D data collected by range finders and a panoramic camera. The complexity of the model is determined during model fitting, by incrementally adding and removing surfaces. In a final post-processing step, measurements are converted into polygons and projected onto the surface model where possible. Empirical results obtained with a mobile robot illustrate that high-resolution models can be acquired in reasonable time.

• Type: Conference
• Year: 2001
• Where: ICML
• Authors: Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard, Sebastian Thrun
• Keywords: Compact 3D Models, Expectation Maximization Algorithm, ICML 2001, Lowcomplexity Planar Model, Machine Learning