Applying domain knowledge to slam using virtual measurements

(c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.Presented at the 2010 IEEE International Conference on Robotics and Automation; Anchorage Convention District; May 3-8, 2010, Anchorage, Alaska, USA.Digital Object Identifier : 10.1109/ROBOT.2010.5509497Simultaneous Localization and Mapping (SLAM) aims to estimate the maximum likelihood map and robot pose based on a robot’s control and sensor measurements. In structured environments, such as human environments, we might have additional domain knowledge that could be applied to produce higher quality mapping results.We present a method for using virtual measurements, which are measurements between two features in our map. To demonstrate this, we present a system that uses such virtual measurements to relate visually detected points to walls detected with a laser scanner.This research has in part been sponsored by ARL MAST CTA, the GT-Boeing Manufacturing Initiative, and the KORUS Cognitive Home Robot Project