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Kniha Electronic Image Stabilization for Mobile Robotic Vision Systems Michael John Smith

Electronic Image Stabilization for Mobile Robotic Vision Systems

Autor: Michael John Smith
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: Biblioscholar
Dostupnost: Skladem u dodavatele
Odesíláme za 9-15 dnů
410 Kč
When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards mo...

Informace o knize

Autor
Michael John Smith
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2012
Stránek
124
EAN
9781288313549
ISBN
9781288313549
Enbook ID
08243080
Vydavatel
Biblioscholar
Hmotnost
236
Rozměry
189 x 246 x 7

Kompletní popis

When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards more complex analysis on the video feed. This thesis presents a novel electronic image stabilization (EIS) algorithm for small inexpensive highly dynamic mobile robotic platforms with onboard camera systems. The algorithm combines optical flow motion parameter estimation with angular rate data provided by a strapdown inertial measurement unit (IMU). A discrete Kalman filter in feedforward configuration is used for optimal fusion of the two data sources. Performance evaluations are conducted by a simulated video truth model (capturing the effects of image translation, rotation, blurring, and moving objects), and live test data. Live data was collected from a camera and IMU affixed to the DAGSI Whegs mobile robotic platform as it navigated through a hallway. Template matching, feature detection, optical flow, and inertial measurement techniques are compared and analyzed to determine the most suitable algorithm for this specific type of image stabilization. Pyramidal Lucas- Kanade optical flow using Shi-Tomasi good features in combination with inertial measurement is the EIS algorithm found to be superior. In the presence of moving objects, fusion of inertial measurement reduces optical flow root-mean-squared(RMS) error in motion parameter estimates by 40%. No previous image stabilization algorithm to date directly fuses optical flow estimation with inertial measurement by way of Kalman filtering.

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