Realtime Modelbased Image Color Correction for Underwater Robots Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic, physic, and learningbased color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.

Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor This paper presents a systematic approach on realtime reconstruction of an underwater environment using Sonar, Visual, Inertial, and Depth data. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes the well defined edges between well lit areas and darkness to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental results in an underwater cave at Ginnie Springs, FL, with a custommade underwater sensor suite demonstrate the performance of our system. This will enable more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions.