In recent decades, visual simultaneous localization and mapping (vSLAM) has gained significant interest in both academia and industry. It estimates camera motion and reconstructs the environment concurrently using visual sensors on a moving robot. However, conventional cameras are limited by hardware, including motion blur and low dynamic range, which can negatively impact performance in challenging scenarios like high-speed motion and high dynamic range illumination. Recent studies have demonstrated that event cameras, a new type of bio-inspired visual sensor, offer advantages such as high temporal resolution, dynamic range, low power consumption, and low latency. This paper presents a timely and comprehensive review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks. The review covers the working principle of event cameras and various event representations for preprocessing event data. It also categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods, with detailed discussions and practical guidance for each approach. Furthermore, the paper evaluates the state-of-the-art methods on various benchmarks, highlighting current challenges and future opportunities in this emerging research area. A public repository will be maintained to keep track of the rapid developments in this field at {\url{https://github.com/kun150kun/ESLAM-survey}}.

翻译：近几十年来，视觉同时定位与地图构建（vSLAM）在学术界和工业界引起了广泛关注。它利用移动机器人上的视觉传感器，同时估计相机运动并重建环境。然而，传统相机受硬件限制，包括运动模糊和低动态范围，这可能会在高速运动和高动态范围光照等挑战性场景中对性能产生负面影响。近期研究表明，事件相机（一种新型仿生视觉传感器）具有高时间分辨率、高动态范围、低功耗和低延迟等优势。本文对利用异步非规则事件流进行定位与地图构建任务的基于事件的vSLAM算法进行了及时且全面的综述。综述涵盖了事件相机的工作原理以及用于预处理事件数据的各种事件表示方法。同时，将基于事件的vSLAM方法分为四大类：基于特征的方法、直接法、运动补偿法和深度学习方法，并对每种方法进行了详细讨论和实用指导。此外，本文在多个基准测试上评估了最先进方法，并指出了这一新兴研究领域当前的挑战和未来机遇。为追踪该领域的快速发展，将在{\url{https://github.com/kun150kun/ESLAM-survey}}维护一个公共资源库。