Aerial-Terrestrial-Aquatic Robots for Search and Rescue in an ATA Extreme Environment

今年，我们完成了项目的一个主要部分，即ATA映射。为了估计水-空过渡过程中的点云，我们提出了一种新的多盒插值方法。本文基于对空中点云和水上点云噪声特性的研究。我们还开发了一种图像处理技术来预测我们是否应该融合点云。这使得机器人在过渡过程中避免碰撞。我们使用热像仪绘制环境图，通过开发一种基于机器学习的基于全光感知的生成对抗网络，用于热图像到彩色图像的转换，帮助机器人在黑暗的水下区域看到。

This year, we completed a major part of our project, focusing on ATA mapping. To estimate point clouds during aerial-aquatic transitions, we developed a new multi-box interpolation method. We based our proposed method on studying the noise characteristics of aerial and aquatic point clouds. We also developed an image processing technique to predict whether we should fuse point clouds or not. This allows the robot to avoid collisions during transitions. We mapped the environment using a thermal camera, which helps the robot to see in dark underwater areas by developed a machine learning-based panoptic perception-based generative adversarial network for thermal-to-color image translation.