Lukierski, RRLukierskiLeutenegger, SSLeuteneggerDavison, AJAJDavison2017-08-042017-07-242017-08-042017-07-24Robotics and Automation (ICRA), 2017 IEEE International Conference on, 2017978-1-5090-4633-1http://hdl.handle.net/10044/1/49081A new generation of practical, low-cost indoor robots is now using wide-angle cameras to aid navigation, but usually this is limited to position estimation via sparse feature-based SLAM. Such robots usually have little global sense of the dimensions, demarcation or identities of the rooms they are in, information which would be very useful to enable behaviour with much more high level intelligence. In this paper we show that we can augment an omni-directional SLAM pipeline with straightforward dense stereo estimation and simple and robust room model fitting to obtain rapid and reliable estimation of the global shape of typical rooms from short robot motions. We have tested our method extensively in real homes, offices and on synthetic data. We also give examples of how our method can extend to making composite maps of larger rooms, and detecting room transitions.© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Conference Paperhttps://www.dx.doi.org/10.1109/ICRA.2017.7989747PO: 4500098215PO 4500285622