面向稀疏数据的可学习地标跟踪的领域自适应与多视图注意力机制
Domain Adaptation and Multi-view Attention for Learnable Landmark Tracking with Sparse Data
AI总结：
提出一种轻量级实时天体地标跟踪系统，通过改进的领域自适应方法和新颖的注意力对齐公式，利用低成本训练数据实现高效检测与跨视点鲁棒描述，性能超越现有最先进技术。
摘要：
天体表面地形特征的检测与跟踪对于自主航天应用至关重要，包括地形相对导航（TRN）、进入、下降与着陆（EDL）、危险分析以及科学数据收集。传统的基于光度测量的流程通常依赖大量的先验成像和离线处理，并受到抗辐射系统计算能力的限制。尽管历史上行之有效，但这些方法通常会提高任务成本和持续时间，处理速率低，且泛化能力有限。近年来，基于学习的计算机视觉技术日益普及，以增强航天器自主性并克服这些限制。尽管前景广阔，新兴技术往往对实时操作的计算需求超出了典型航天器硬件的能力，并进一步受到多样地外环境标注训练数据稀缺的挑战。在本研究中，我们提出了通过检测与描述进行原位地标跟踪的新颖公式。我们采用轻量级、计算高效的神经网络架构，专为在当前一代航天器飞行处理器上实时执行而设计。对于地标检测，我们提出了改进的领域自适应方法，能够利用独特且低成本获取的训练数据来识别天体地形特征。同时，对于地标描述，我们引入了一种新颖的注意力对齐公式，学习鲁棒的特征表示，即使地标视点发生显著变化也能保持对应关系。这些贡献共同构成了一个统一的地标跟踪系统，与现有最先进技术相比展现出更优越的性能。
英文摘要：
The detection and tracking of celestial surface terrain features are crucial for autonomous spaceflight applications, including Terrain Relative Navigation (TRN), Entry, Descent, and Landing (EDL), hazard analysis, and scientific data collection. Traditional photoclinometry-based pipelines often rely on extensive a priori imaging and offline processing, constrained by the computational limitations of radiation-hardened systems. While historically effective, these approaches typically increase mission costs and duration, operate at low processing rates, and have limited generalization. Recently, learning-based computer vision has gained popularity to enhance spacecraft autonomy and overcome these limitations. While promising, emerging techniques frequently impose computational demands exceeding the capabilities of typical spacecraft hardware for real-time operation and are further challenged by the scarcity of labeled training data for diverse extraterrestrial environments. In this work, we present novel formulations for in-situ landmark tracking via detection and description. We utilize lightweight, computationally efficient neural network architectures designed for real-time execution on current-generation spacecraft flight processors. For landmark detection, we propose improved domain adaptation methods that enable the identification of celestial terrain features with distinct, cheaply acquired training data. Concurrently, for landmark description, we introduce a novel attention alignment formulation that learns robust feature representations that maintain correspondence despite significant landmark viewpoint variations. Together, these contributions form a unified system for landmark tracking that demonstrates superior performance compared to existing state-of-the-art techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
