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Journal of Control Science and Engineering
Volume 2011 (2011), Article ID 413074, 10 pages
http://dx.doi.org/10.1155/2011/413074
Research Article

Parallel Tracking and Mapping for Controlling VTOL Airframe

1Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
2Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506, USA

Received 29 April 2011; Revised 7 September 2011; Accepted 12 September 2011

Academic Editor: Onur Toker

Copyright © 2011 Michal Jama and Dale Schinstock. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This work presents a vision based system for navigation on a vertical takeoff and landing unmanned aerial vehicle (UAV). This is a monocular vision based, simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video stream from a single camera. This is different from past SLAM solutions on UAV which use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. Solution presented in this paper extends and significantly modifies a recent open-source algorithm that solves SLAM problem using approach fundamentally different from a traditional approach. Proposed modifications provide the position measurements necessary for the navigation solution on a UAV. The main contributions of this work include: (1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; (2) improved performance of the SLAM algorithm for lower camera frame rates; and (3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible.