Applying domain knowledge to SLAM using virtual measurements

Abstract

Simultaneous 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.