Underwater docking is critical to enable the persistent operation of
Autonomous Underwater Vehicles (AUVs). For this, the AUV must be capable of
detecting and localizing the docking station, which is complex due to the
highly dynamic undersea environment. Image-based solutions offer a high
acquisition rate and versatile alternative to adapt to this environment;
however, the underwater environment presents challenges such as low visibility,
high turbidity, and distortion. In addition to this, field experiments to
validate underwater docking capabilities can be costly and dangerous due to the
specialized equipment and safety considerations required to conduct the
experiments. This work compares different deep-learning architectures to
perform underwater docking detection and classification. The architecture with
the best performance is then compressed using knowledge distillation under the
teacher-student paradigm to reduce the network's memory footprint, allowing
real-time implementation. To reduce the simulation-to-reality gap, a Generative
Adversarial Network (GAN) is used to do image-to-image translation, converting
the Gazebo simulation image into a realistic underwater-looking image. The
obtained image is then processed using an underwater image formation model to
simulate image attenuation over distance under different water types. The
proposed method is finally evaluated according to the AUV docking success rate
and compared with classical vision methods. The simulation results show an
improvement of 20% in the high turbidity scenarios regardless of the underwater
currents. Furthermore, we show the performance of the proposed approach by
showing experimental results on the off-the-shelf AUV Iver3.

比较不同的深度学习架构以实现水下对接检测和分类，并使用知识蒸馏压缩具有最佳性能的架构，利用生成对抗网络进行图像转换以模拟真实水下图像，最终评估该方法的成功率并与传统视觉方法进行比较。

水下对接的高效检测与控制系统：基于机器学习和逼真仿真的综合方法

An Efficient Detection and Control System for Underwater Docking using  Machine Learning and Realistic Simulation: A Comprehensive Approach

Ensuring the safe and reliable operation of autonomous vehicles under adverse
weather remains a significant challenge. To address this, we have developed a
comprehensive dataset composed of sensor data acquired in a real test track and
reproduced in the laboratory for the same test scenarios. The provided dataset
includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data
recorded under adverse weather conditions (rainy, night-time, and snowy
conditions). We recorded test scenarios using objects of interest such as car,
cyclist, truck and pedestrian -- some of which are inspired by EURONCAP
(European New Car Assessment Programme). The sensor data generated in the
laboratory is acquired by the execution of simulation-based tests in
hardware-in-the-loop environment with the digital twin of each real test
scenario. The dataset contains more than 2 hours of recording, which totals
more than 280GB of data. Therefore, it is a valuable resource for researchers
in the field of autonomous vehicles to test and improve their algorithms in
adverse weather conditions, as well as explore the simulation-to-reality gap.
The dataset is available for download at: this https URL

确保自动驾驶汽车在恶劣天气下的安全可靠运行仍然是一个重大挑战，为此我们开发了一个全面的数据集，其中包括在真实测试场地和实验室中进行相同测试场景的传感器数据。该数据集包括在恶劣的天气条件下（雨天、夜间和雪天）记录的摄像头、雷达、激光雷达、惯性测量单元（IMU）和 GPS 数据。该数据集是一个宝贵的资源，为自动驾驶车辆领域的研究人员提供了在恶劣天气条件下测试和改进算法以及探索仿真与现实之间差距的机会。

TWICE 数据集：受控环境中的测试场景数字孪生

TWICE Dataset: Digital Twin of Test Scenarios in a Controlled  Environment

Accurate diagnosis of propeller faults is crucial for ensuring the safe and
efficient operation of quadrotors. Training a fault classifier using simulated
data and deploying it on a real quadrotor is a cost-effective and safe
approach. However, the simulation-to-reality gap often leads to poor
performance of the classifier when applied in real flight. In this work, we
propose a deep learning model that addresses this issue by utilizing newly
identified features (NIF) as input and utilizing domain adaptation techniques
to reduce the simulation-to-reality gap. In addition, we introduce an adjusted
simulation model that generates training data that more accurately reflects the
behavior of real quadrotors. The experimental results demonstrate that our
proposed approach achieves an accuracy of 96\% in detecting propeller faults.
To the best of our knowledge, this is the first reliable and efficient method
for simulation-to-reality fault diagnosis of quadrotor propellers.

本研究提出了一种深度学习模型，利用新识别的特征（NIF）作为输入和利用域自适应技术来减少仿真到现实的差距，进而实现了四旋翼螺旋桨故障检测方面的模拟与现实诊断手段之间转换的目的。实验结果表明，提出的方法在检测螺旋桨故障方面具有高达 96% 的准确率，目前是一种可靠且高效的方法。