EAGER: Toward Descriptive Mapping for Underwater Exploration

该项目将为以稀疏和噪声数据为特征的水下环境的3D测绘和勘探提供一种新的解决方案。它将增强水下机器人自主绘制地图、导航和检查以前未知的浅水环境的能力。这个项目的发展将得益于它与美国海岸警卫队的长期合作。该项目将促进使用高斯过程回归的三维空间地图绘制的最新技术，这是一种监督学习方法，在机器人地图绘制中具有很大的应用前景。该项目将把这些方法应用于现场机器人的在线3D地图绘制，使用扫描声纳绘制水下结构图。

This project will provide a new solution for 3D mapping and exploration of underwater environments characterized by sparse and noisy data. It will enhance the capability of underwater robots to autonomously map, navigate, and inspect a previously unknown shallow-water environment. The development of this project will benefit from its long association with United States Coast Guard.This project will advance the state of the art in three-dimensional occupancy mapping using Gaussian process regression, a supervised learning method with great promise for its application to robot mapping. This project will apply these methods to online 3D mapping with a field robot, mapping underwater structures with a scanning sonar.