Date of Award
2013
Degree Type
Thesis
Degree Name
Master of Science in Ocean Engineering
Department
Ocean Engineering
First Advisor
Chris Roman
Abstract
A Lagrangian float with bottom-imaging cameras is used for benthic surveys as it drifts a nominally constant altitude over the bottom. To maintain constant spatial sampling, the camera capture rate must be adjusted in real time based on vehicle speed. This speed is difficult to measure with conventional sensors, but can be found from the survey images using visual odometry. A featureless technique is used due to its increased robustness to noise and focus errors over feature-matching, along with a faster and more consistent computation time. A stereo pair of images taken at each vehicle location is used to find altitude. Then, the image from one camera is registered to the same camera's previous image with phase correlation, correcting for rotation and scale differences using a log-polar transformation. This registration is combined with known camera geometry to find vehicle motion and speed between imaging positions.
Registration is validated with float images having known offsets, and visual odometry is compared with ground-truthed ROV surveys. Odometry is performed successfully using data from float surveys. Low image overlap and high bottom roughness decrease the probability of successful matches, but these are overcome by slightly higher capture rates. Further, incorrect matches are easily identified and rejected, with minimal impact on the vehicle velocity estimate.
Image scheduling is simulated using a high framerate dataset and allowing the algorithm to select images taken at times separated approximately by its desired image period. Computation time is sufficiently short and consistent enough to keep up with image acquisition in real time. Average power and data storage requirements are decreased, allowing for longer and more frequent surveys.
Recommended Citation
Casagrande, David S., "REAL-TIME FEATURELESS VISUAL ODOMETRY FOR SEA FLOOR IMAGING WITH A LAGRANGIAN FLOAT" (2013). Open Access Master's Theses. Paper 159.
https://digitalcommons.uri.edu/theses/159
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