Spatial Uncertainty Management for Simultaneous Localization and Mapping

Abstract

In this paper we discuss methods to reduce spatial uncertainty in the simultaneous localization and mapping (SLAM) procedure for a mobile robot equipped with a 2D laser scanner and operating in a structured, but non-static environment. We augment the classic EKF-based SLAM procedure with two new modules. The first one reliably extracts line segments from the laser scans, employing a novel fuzzy-set-based grid map. The second one corrects the robot odometry by using scan matching. Both modules rely on a laser scanner measurement model, which covers both the quantitative and qualitative types of uncertainty.