Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments
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Roy_Estimation, planning and mapping.pdf
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Author(s)
Bachrach, Abraham Galton
Prentice, Samuel James
He, Ruijie
Huang, Albert S.
Roy, Nicholas
Henry, Peter
Krainin, Michael
Maturana, Daniel
Fox, Dieter
Date Issued
September 2012
Journal
The International Journal of Robotics Research
Publisher
Sage Publications
Citation
Bachrach, A., S. Prentice, R. He, P. Henry, A. S. Huang, M. Krainin, D. Maturana, D. Fox, and N. Roy. “Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments.” The International Journal of Robotics Research 31, no. 11 (September 11, 2012): 1320-1343.
Version
Author's final manuscript
Abstract
RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent state-of-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on an unreliable wireless link to a ground station. However, even with accurate 3D sensing and position estimation, some parts of the environment have more perceptual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost or worse. We show how the belief roadmap algorithm prentice2009belief, a belief space extension of the probabilistic roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effectiveness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1177/0278364912455256
