THESIS
2017
xi, 55 pages : illustrations ; 30 cm
Abstract
Autonomous micro aerial vehicles (MAVs) have cost and mobility benefits, making them
ideal robotic platforms for applications including aerial photography, surveillance, and
search and rescue. As the platform scales down, MAVs become more capable of
operating in confined environments, but they also introduce challenges such as
environment perception using the minimum sensor suite and state estimation under the
aggressive motion. In fact, a monocular camera together with an inertial measurement
unit (IMU) becomes the minimum sensor suite allowing autonomous
flight with sufficient
environmental awareness. With more agility, there comes more serious motion blur in
the images which disrupts the classic visual-based localization methods. In this thesis,
we firstly present a GPU-ac...[
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Autonomous micro aerial vehicles (MAVs) have cost and mobility benefits, making them
ideal robotic platforms for applications including aerial photography, surveillance, and
search and rescue. As the platform scales down, MAVs become more capable of
operating in confined environments, but they also introduce challenges such as
environment perception using the minimum sensor suite and state estimation under the
aggressive motion. In fact, a monocular camera together with an inertial measurement
unit (IMU) becomes the minimum sensor suite allowing autonomous
flight with sufficient
environmental awareness. With more agility, there comes more serious motion blur in
the images which disrupts the classic visual-based localization methods. In this thesis,
we firstly present a GPU-accelerated monocular dense mapping that conditions on the
estimated pose providing wide-angle situational awareness. Through a truncated signed
distance function (TSDF) fusion, a global dense mesh map is fused for autonomous
flight as well as depth perception for blur-aware motion estimation. A Spline-based pose
representation is then adopted and optimized using both blurred images and IMU
measurements. Extensive experimental results are provided to validate individual
modules. Finally, an autonomous navigation application based on our dense mapping is
presented, the overall performance in both indoor and outdoor environments are shown.
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