Search Results for author:
Found 7 papers, 3 papers with code
Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles
This paper introduces the first publicly accessible multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs).
Improving the perception of visual fiducial markers in the field using Adaptive Active Exposure Control
Accurate localization is fundamental for autonomous underwater vehicles (AUVs) to carry out precise tasks, such as manipulation and construction.
Real-Time Dense 3D Mapping of Underwater Environments
To address this problem, we propose to use SVIn2, a robust VIO method, together with a real-time 3D reconstruction pipeline.
DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image.
Real-time Model-based 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.
Robotics
Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain
A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data.
Robotics
Multi-robot Dubins Coverage with Autonomous Surface Vehicles
In particular, we propose a novel approach for solving the problem of complete coverage of a known environment by a multi-robot team consisting of Dubins vehicles.
