面向目标导向导航的多模态感知：综述
Multimodal Perception for Goal-oriented Navigation: A Survey
AI总结：
本综述从推理域视角系统梳理多模态（视觉、语言、声学）目标导向导航方法，分析共享计算基础与整合挑战，并回顾约200篇文献，为提升导航能力提供全面参考。
摘要：
目标导向导航对自主系统提出了根本性挑战，要求智能体在复杂环境中导航以到达指定目标。本综述通过推理域的统一视角，对多模态导航方法进行了全面分析，探讨了智能体如何利用视觉、语言和声学信息来感知、推理并导航环境。我们的主要贡献包括：基于跨推理域的主要环境推理机制对导航方法进行组织；系统分析共享的计算基础如何支撑不同导航任务中看似不同的方法；识别各种导航范式中反复出现的模式与独特优势；以及审视多模态感知在增强导航能力方面的整合挑战与机遇。此外，我们回顾了约200篇相关文章，以深入理解当前研究格局。
英文摘要：
Goal-oriented navigation presents a fundamental challenge for autonomous systems, requiring agents to navigate complex environments to reach designated targets. This survey offers a comprehensive analysis of multimodal navigation approaches through the unifying perspective of inference domains, exploring how agents perceive, reason about, and navigate environments using visual, linguistic, and acoustic information. Our key contributions include organizing navigation methods based on their primary environmental reasoning mechanisms across inference domains; systematically analyzing how shared computational foundations support seemingly disparate approaches across different navigation tasks; identifying recurring patterns and distinctive strengths across various navigation paradigms; and examining the integration challenges and opportunities of multimodal perception to enhance navigation capabilities. In addition, we review approximately 200 relevant articles to provide an in-depth understanding of the current landscape.
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。
