事件相机与移动具身感知：抽象、算法、加速与应用
Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application
AI总结：
本文综述了基于事件的移动传感技术，涵盖其原理、抽象方法、算法进展及加速策略，并探讨了其在视觉里程计、目标跟踪等应用中的挑战与未来发展方向。
摘要：
随着移动设备应用的日益复杂化，这些设备正朝着高敏捷性发展。这种转变对移动传感提出了新的要求，特别是在实现高精度和低延迟方面。基于事件的视觉已成为一种颠覆性的范式，提供了高时间分辨率和低延迟，使其非常适合在高敏捷平台上进行高精度和低延迟的传感任务。然而，大量噪声事件、缺乏稳定持久的语义信息以及大数据量对事件数据处理在资源受限的移动设备上构成了挑战。本文综述了2014年至2025年的文献，并全面概述了基于事件的移动传感，涵盖其基本原理、事件抽象方法、算法进展以及硬件和软件的加速策略。我们讨论了事件相机在移动传感中的关键应用，包括视觉里程计、目标跟踪、光流和3D重建，同时突出了与事件数据处理、传感器融合和实时部署相关的挑战。此外，我们还概述了未来的研究方向，如通过先进光学改进事件相机、利用神经形态计算实现高效处理，以及整合生物启发算法。为了支持持续研究，我们提供了一个开源的在线表格来展示最近的发展。我们希望这篇综述能成为参考，促进事件视觉在各种应用中的采用。
英文摘要：
With the increasing complexity of mobile device applications, these devices are evolving toward high agility. This shift imposes new demands on mobile sensing, particularly in achieving high-accuracy and low-latency. Event-based vision has emerged as a disruptive paradigm, offering high temporal resolution and low latency, making it well-suited for high-accuracy and low-latency sensing tasks on high-agility platforms. However, the presence of substantial noisy events, lack of stable, persistent semantic information, and large data volume pose challenges for event-based data processing on resource-constrained mobile devices. This paper surveys the literature from 2014 to 2025 and presents a comprehensive overview of event-based mobile sensing, encompassing its fundamental principles, event \textit{abstraction} methods, \textit{algorithm} advancements, and both hardware and software \textit{acceleration} strategies. We discuss key \textit{applications} of event cameras in mobile sensing, including visual odometry, object tracking, optical flow, and 3D reconstruction, while highlighting challenges associated with event data processing, sensor fusion, and real-time deployment. Furthermore, we outline future research directions, such as improving the event camera with advanced optics, leveraging neuromorphic computing for efficient processing, and integrating bio-inspired algorithms. To support ongoing research, we provide an open-source \textit{Online Sheet} with recent developments. We hope this survey serves as a reference, facilitating the adoption of event-based vision across diverse applications.
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。
