SLAM for mobile robots using laser range finder and monocular vision

Localization and map building are two essential tasks for an autonomous mobile robot’s indoor navigation without a prior map. This paper describes a mobile robot system designed for simultaneous localization and mapping (SLAM) for an autonomous mobile robot in an indoor environment. Due to variant sensor modeling for laser range finder and CCD camera, weighted least square fitting and Canny operator are used to extract certain two-dimensional environmental features and vertical edges respectively. Using Kalman filtering (KF) to localization and grid map building simultaneously are also presented. When implemented on a Zixing mobile robot produced by Harbin Institute of Technology (Weihai), the localization technique correctly localized the robot while exploring and mapping.