基于可迁移视觉词袋与绝对-相对特征的开放世界分布式机器人自定位
Open-World Distributed Robot Self-Localization with Transferable Visual Vocabulary and Both Absolute and Relative Features
AI总结：
针对开放世界分布式机器人系统中视觉知识迁移共享不足的问题，提出兼具绝对与相对特征的轻量化可迁移无监督视觉词袋自定位框架，在主被动定位场景中均验证有效。
摘要：
视觉机器人自定位是视觉机器人导航中的基础问题，已在单目定位、序列定位等多种问题设置下得到研究。但现有研究大多聚焦单机器人场景，对涉及通过通信容量受限的无线网络连接的异构机器人的通用场景（如开放世界分布式机器人系统）探索有限，尤其是机器人间视觉描述、视觉词袋等关键知识的迁移与共享问题长期被忽视。本工作提出一种面向开放世界分布式机器人系统的新型自定位框架，在保持当前最优性能的同时具备两大核心优势：（1）采用无监督视觉词袋模型，可映射至多模态、轻量化且可迁移的视觉特征；（2）视觉词袋本身为轻量化、通信友好型模型。尽管框架主要聚焦单目视角图像编码，也可轻松扩展至序列定位应用。通过利用互补的保相似性特征——绝对特征与相对特征兼具——该框架满足无监督、多模态、轻量化、可迁移的要求。所有特征均通过轻量化图神经网络与场景图进行学习与识别。所提方法的有效性在被动与主动自定位场景中均得到验证。
英文摘要：
Visual robot self-localization is a fundamental problem in visual robot navigation and has been studied across various problem settings, including monocular and sequential localization. However, many existing studies focus primarily on single-robot scenarios, with limited exploration into general settings involving diverse robots connected through wireless networks with constrained communication capacities, such as open-world distributed robot systems. In particular, issues related to the transfer and sharing of key knowledge, such as visual descriptions and visual vocabulary, between robots have been largely neglected. This work introduces a new self-localization framework designed for open-world distributed robot systems that maintains state-of-the-art performance while offering two key advantages: (1) it employs an unsupervised visual vocabulary model that maps to multimodal, lightweight, and transferable visual features, and (2) the visual vocabulary itself is a lightweight and communication-friendly model. Although the primary focus is on encoding monocular view images, the framework can be easily extended to sequential localization applications. By utilizing complementary similarity-preserving features -- both absolute and relative -- the framework meets the requirements for being unsupervised, multimodal, lightweight, and transferable. All features are learned and recognized using a lightweight graph neural network and scene graph. The effectiveness of the proposed method is validated in both passive and active self-localization scenarios.
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。
