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

Abstract

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

Keywords

Mobile robotComputer scienceRobotRange (aeronautics)PlanarMaximizationComputer visionArtificial intelligenceExpectation–maximization algorithmAlgorithmComputer graphics (images)Mathematical optimizationMaximum likelihoodMathematicsEngineering

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Publication Info

Year: 2001
Type: article
Pages: 329-336
Citations: 146
Access: Closed

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APA Style

                            
                                    Yufeng Liu, 
                                
                                    Rosemary Emery, 
                                
                                    Deepayan Chakrabarti
                                
                                et al.
                            
                            (2001). 
                            Using EM to Learn 3D Models of Indoor Environments with Mobile Robots. 
                            
                            , 329-336.