Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness, offering notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this paper presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly, investigating fusion strategies that incorporate frame-based cameras, inertial measurement units (IMUs), and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications.

本研究针对现有里程计应用中常规传感器的局限性，提出了一种新颖的基于事件的传感器融合方法，特别关注将其与框架摄像头、惯性测量单元和激光雷达结合的策略。研究发现，这种融合方法显著提高了在复杂环境中的里程计性能，为未来的研究方向提供了新视角。

基于事件的传感器融合及其在里程计中的应用：综述