Various animals exhibit accurate navigation using environment cues. The
Earth's magnetic field has been proved a reliable information source in
long-distance fauna migration. Inspired by animal navigation, this work
proposes a bionic and data-driven approach for long-distance underwater
navigation. The proposed approach uses measured geomagnetic data for the
navigation, and requires no GPS systems or geographical maps. Particularly, we
construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM)
network to predict the heading angle during the navigation. To mitigate the
impact of geomagnetic anomalies, we develop the mechanism to detect and
quantify the anomalies based on Maximum Likelihood Estimation. We integrate the
developed mechanism with the TA-LSTM, and calibrate the predicted heading
angles to gain resistance against geomagnetic anomalies. Using the retrieved
data from the WMM model, we conduct numerical simulations with diversified
navigation conditions to test our approach. The simulation results demonstrate
a resilience navigation against geomagnetic anomalies by our approach, along
with precision and stability of the underwater navigation in single and
multiple destination missions.

通过测量地磁数据，该研究构建和训练了一种基于时间注意力的长短期记忆网络，用于长距离水下导航，并通过最大似然估计检测并量化地磁异常，以提高导航的准确性和稳定性。

一种仿生数据驱动的远距离水下导航方法和抗异常技术

A Bionic Data-driven Approach for Long-distance Underwater Navigation  with Anomaly Resistance

Polarization information of the light can provide rich cues for computer
vision and scene understanding tasks, such as the type of material, pose, and
shape of the objects. With the advent of new and cheap polarimetric sensors,
this imaging modality is becoming accessible to a wider public for solving
problems such as pose estimation, 3D reconstruction, underwater navigation, and
depth estimation. However, we observe several limitations regarding the usage
of this sensorial modality, as well as a lack of standards and publicly
available tools to analyze polarization images. Furthermore, although
polarization camera manufacturers usually provide acquisition tools to
interface with their cameras, they rarely include processing algorithms that
make use of the polarization information. In this paper, we review recent
advances in applications that involve polarization imaging, including a
comprehensive survey of recent advances on polarization for vision and robotics
perception tasks. We also introduce a complete software toolkit that provides
common standards to communicate with and process information from most of the
existing micro-grid polarization cameras on the market. The toolkit also
implements several image processing algorithms for this modality, and it is
publicly available on GitHub: this https URL

偏振成像在计算机视觉和场景理解任务中可以提供丰富的线索，如物料类型、姿态和物体形状。本文回顾了涉及偏振成像的应用的最新进展，并介绍了一个完整的软件工具包，用于与市场上大多数现有微网偏振相机进行通信和处理信息。