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.
General
| Imprint: |
Biblioscholar
|
| Country of origin: |
United States |
| Release date: |
November 2012 |
| First published: |
November 2012 |
| Authors: |
Michael John Smith
|
| Dimensions: |
246 x 189 x 7mm (L x W x T) |
| Format: |
Paperback - Trade
|
| Pages: |
124 |
| ISBN-13: |
978-1-288-31354-9 |
| Categories: |
Books >
Social sciences >
Education >
General
Promotions
|
| LSN: |
1-288-31354-3 |
| Barcode: |
9781288313549 |
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