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Autonomous Landing and Ingress of Micro-Air-Vehicles in Urban Environments Based on Monocular VisionUnmanned micro air vehicles (MAVs) will play an important role in future reconnaissance and search and rescue applications. In order to conduct persistent surveillance and to conserve energy, MAVs need the ability to land, and they need the ability to enter (ingress) buildings and other structures to conduct reconnaissance. To be safe and practical under a wide range of environmental conditions, landing and ingress maneuvers must be autonomous, using real-time, onboard sensor feedback. To address these key behaviors, we present a novel method for vision-based autonomous MAV landing and ingress using a single camera for two urban scenarios: landing on an elevated surface, representative of a rooftop, and ingress through a rectangular opening, representative of a door or window. Real-world scenarios will not include special navigation markers, so we rely on tracking arbitrary scene features; however, we do currently exploit planarity of the scene. Our vision system uses a planar homography decomposition to detect navigation targets and to produce approach waypoints as inputs to the vehicle control algorithm. Scene perception, planning, and control run onboard in real-time; at present we obtain aircraft position knowledge from an external motion capture system, but we expect to replace this in the near future with a fully self-contained, onboard, vision-aided state estimation algorithm. We demonstrate autonomous vision-based landing and ingress target detection with two different quadrotor MAV platforms. To our knowledge, this is the first demonstration of onboard, vision-based autonomous landing and ingress algorithms that do not use special purpose scene markers to identify the destination.
Document ID
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Brockers, Roland
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Bouffard, Patrick
(California Univ. Berkeley, CA, United States)
Ma, Jeremy
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Matthies, Larry
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Tomlin, Claire
(California Univ. Berkeley, CA, United States)
Date Acquired
August 26, 2013
Publication Date
April 25, 2011
Subject Category
Aircraft Stability And Control
Meeting Information
Meeting: SPIE Defense, Security, and Sensing
Location: Orlando, FL
Country: United States
Start Date: April 25, 2011
Sponsors: International Society for Optical Engineering
Distribution Limits
micro air vehicles (MAVs)
ingress algorithms
vision-based autonomous landing

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