Event-based Sensor Fusion and Application on Odometry: A Survey
Jiaqiang Zhang, Xianjia Yu, Ha Sier, Haizhou Zhang, Tomi Westerlund
TL;DR
The paper addresses odometry robustness under challenging lighting and fast motion by surveying event-based sensor fusion approaches. It surveys methods that combine event cameras with frame cameras, IMUs, stereo setups, and LiDAR, including event-only VO, event–frame fusion, and event-based stereo visual odometry, along with relevant datasets. Key contributions include a structured taxonomy of fusion strategies, critical assessment of their benefits and limitations, and a discussion of open challenges such as asynchronous data processing and sensor calibration. The work highlights the potential of event cameras to enhance real-time, robust odometry for next-generation robotics in dynamic environments.
Abstract
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.