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Carnegie Mellon Robotics Institute

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Abstract

Autonomous control of small and micro air vehicles (SMAV) requires precise estimation of both vehicle state and its surrounding environment. Small cameras, which are available today at very low cost, are attractive sensors for SMAV. 3D vision by video and laser scanning has distinct advantages in that they provide positional information relative to objects and environments, in which the vehicle operates, that is critical to obstacle avoidance and mapping of the environment. This paper presents work on real-time 3D vision algorithms for recovering motion and structure from a video sequence, 3D terrain mapping from a laser range finder onboard a small autonomous helicopter, and sensor fusion of visual and GPS/INS sensors.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Helicopter Lab

Number of pages: 8

Text Reference

Takeo Kanade, Omead Amidi, and Qifa Ke, "Real-Time and 3D Vision for Autonomous Small and Micro Air Vehicles," 43rd IEEE Conference on Decision and Control (CDC 2004), December, 2004.

BibTeX Reference

@inproceedings{Kanade_2004_5241,
   author = "Takeo Kanade and Omead Amidi and Qifa Ke",
   title = "Real-Time and 3D Vision for Autonomous Small and Micro Air Vehicles",
   booktitle = "43rd IEEE Conference on Decision and Control (CDC 2004)",
   month = "December",
   year = "2004",
}

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