國立虎尾科技大學 |

Unlocking Dynamic Cameras for Visual Navigation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Unlocking Dynamic Cameras for Visual Navigation./
作者:	Rebello, Jason Joseph.
面頁冊數:	1 online resource (147 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Aerospace engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798379765170

Unlocking Dynamic Cameras for Visual Navigation.
Rebello, Jason Joseph.

Unlocking Dynamic Cameras for Visual Navigation. - 1 online resource (147 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2023.

Includes bibliographical references

The ability of a mobile robot to navigate reliably in an unknown environment depends on the information perceived from its surroundings, usually available in the form of measurements from sensors. However, most of the current sensor configurations assume a static rigid transformation between the sensors, placing many limitations on its performance, especially when undergoing high dynamic motion profiles. A Dynamic Camera consists of a camera mounted to an actuated mechanism such as a gimbal and offers several advantages compared to the static case. However, In order to accurately relate information across these cameras and other sensors, a precise time-varying extrinsic calibration needs to be determined. In this dissertation, I present novel methods to calibrate a Dynamic Camera to other sensors such as static cameras and IMUs. First, I analyze a Dynamic Camera Cluster system, and present an information theoretic Next-Best-View formulation that directly minimizes the uncertainty on the calibration parameters and test the resulting calibration in a visual-inertial odometry application. Second, I present a novel analysis of the redundant parameters in the calibration system when joint angle values to the actuated mechanism are unavailable and propose a novel minimal parameterization to resolve the degeneracies. Third, I present the online calibration between a Dynamic Camera and an IMU, and analyze the observability properties of the calibration parameters. I perform extensive tests in simulation and in the real world, consisting of a 3-DOF gimbal and a 5-DOF manipulator, demonstrating our ability to recover an accurate calibration, thereby allowing Dynamic Cameras to be used in visual navigation applications.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379765170Subjects--Topical Terms:

686400
Aerospace engineering.
Subjects--Index Terms:

CalibrationIndex Terms--Genre/Form:

554714
Electronic books.

Unlocking Dynamic Cameras for Visual Navigation.
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520 $a The ability of a mobile robot to navigate reliably in an unknown environment depends on the information perceived from its surroundings, usually available in the form of measurements from sensors. However, most of the current sensor configurations assume a static rigid transformation between the sensors, placing many limitations on its performance, especially when undergoing high dynamic motion profiles. A Dynamic Camera consists of a camera mounted to an actuated mechanism such as a gimbal and offers several advantages compared to the static case. However, In order to accurately relate information across these cameras and other sensors, a precise time-varying extrinsic calibration needs to be determined. In this dissertation, I present novel methods to calibrate a Dynamic Camera to other sensors such as static cameras and IMUs. First, I analyze a Dynamic Camera Cluster system, and present an information theoretic Next-Best-View formulation that directly minimizes the uncertainty on the calibration parameters and test the resulting calibration in a visual-inertial odometry application. Second, I present a novel analysis of the redundant parameters in the calibration system when joint angle values to the actuated mechanism are unavailable and propose a novel minimal parameterization to resolve the degeneracies. Third, I present the online calibration between a Dynamic Camera and an IMU, and analyze the observability properties of the calibration parameters. I perform extensive tests in simulation and in the real world, consisting of a 3-DOF gimbal and a 5-DOF manipulator, demonstrating our ability to recover an accurate calibration, thereby allowing Dynamic Cameras to be used in visual navigation applications.
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538 $a Mode of access: World Wide Web
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650 4 $a Robotics. $3 561941
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653 $a Drones
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653 $a Gimbal cameras
653 $a State estimation
653 $a Visual SLAM
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