With the increasing complexity of mobile device applications, these devices are evolving toward high mobility. This shift imposes new demands on mobile sensing, particularly in terms of achieving high accuracy and low latency. Event-based vision has emerged as a disruptive paradigm, offering high temporal resolution, low latency, and energy efficiency, making it well-suited for high-accuracy and low-latency sensing tasks on high-mobility platforms. However, the presence of substantial noisy events, the lack of inherent semantic information, and the large data volume pose significant challenges for event-based data processing on resource-constrained mobile devices. This paper surveys the literature over the period 2014-2024, provides a comprehensive overview of event-based mobile sensing systems, covering fundamental principles, event abstraction methods, algorithmic advancements, hardware and software acceleration strategies. We also discuss key applications of event cameras in mobile sensing, including visual odometry, object tracking, optical flow estimation, and 3D reconstruction, while highlighting the challenges associated with event data processing, sensor fusion, and real-time deployment. Furthermore, we outline future research directions, such as improving event camera hardware with advanced optics, leveraging neuromorphic computing for efficient processing, and integrating bio-inspired algorithms to enhance perception. To support ongoing research, we provide an open-source \textit{Online Sheet} with curated resources and recent developments. We hope this survey serves as a valuable reference, facilitating the adoption of event-based vision across diverse applications.

翻译：随着移动设备应用日益复杂，这些设备正朝着高机动性方向演进。这一转变对移动传感提出了新的要求，特别是在实现高精度与低延迟方面。事件视觉作为一种颠覆性范式应运而生，其具备高时间分辨率、低延迟和能量高效的特点，非常适合高机动性平台上的高精度低延迟传感任务。然而，大量噪声事件的存在、固有语义信息的缺失以及庞大的数据量，给资源受限移动设备上的事件数据处理带来了重大挑战。本文综述了2014年至2024年间的文献，全面概述了基于事件的移动传感系统，涵盖基本原理、事件抽象方法、算法进展、硬件与软件加速策略。我们还探讨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、光流估计和三维重建，同时重点分析了事件数据处理、传感器融合和实时部署所面临的挑战。此外，我们展望了未来研究方向，例如通过先进光学技术改进事件相机硬件、利用神经形态计算实现高效处理，以及整合仿生算法以增强感知能力。为支持持续研究，我们提供了一个包含精选资源和最新进展的开源 \textit{在线表格}。我们希望本综述能成为有价值的参考，推动事件视觉技术在不同应用领域的广泛采纳。