Shallow Coral Classification Using Deep Learning

Coral reef is very integral to marine ecology. With advancement in underwater autonomous vehicles, large amount of data are being collected. The data requires annotation before they are used by marine biologist. In this project, we focus on using deep learning techniques to automate the annotation.
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Experimental Comparison of open source Vision based State Estimation Algorithms

In robot autonomy, estimating state and mapping, popularly known as Simultaneous Localization and Mapping (SLAM), is an active research field. There has been many works trying to solve SLAM problem, yet there exists no method that can be called robust such as it works in all environment and conditions. In this comparative study, we take recent and popular SLAM systems avaliable open source and try them on our datasets. These datasets are created as if they represent different environments with different conditions i.e. illumination, for multiple robots.
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Curiously Exploring Coral Reefs and Mapping Using Autonomous Underwater Vehicles

This work focuses on planning path of underwater autonomous vehicles such that they find coral reef and maps the entire connected reef. Once mapping is finished, the robot goes outward in a systematic way to find nearby coral reef while trying not to get lost.