2D simultaneous localization and mapping for unmanned aerial vehicles

Abstract

In this work, a 2D vision-based Simultaneous Localization and Mapping algorithm is developed for an Unmanned Aerial Vehicle flying at a constant altitude. We use visual features of images obtained from an on-board camera to identify different landmarks. Using these landmarks we apply the well-known Extended Kalman Filter to the SLAM problem and present some simulation results. ©2008 IEEE.