Event-based cameras asynchronously capture individual visual changes in a
scene. This makes them more robust than traditional frame-based cameras to
highly dynamic motions and poor illumination. It also means that every
measurement in a scene can occur at a unique time.
Handling these different measurement times is a major challenge of using
event-based cameras. It is often addressed in visual odometry (VO) pipelines by
approximating temporally close measurements as occurring at one common time.
This grouping simplifies the estimation problem but sacrifices the inherent
temporal resolution of event-based cameras.
This paper instead presents a complete stereo VO pipeline that estimates
directly with individual event-measurement times without requiring any grouping
or approximation. It uses continuous-time trajectory estimation to maintain the
temporal fidelity and asynchronous nature of event-based cameras through
Gaussian process regression with a physically motivated prior. Its performance
is evaluated on the MVSEC dataset, where it achieves 7.9e-3 and 5.9e-3 RMS
relative error on two independent sequences, outperforming the existing
publicly available event-based stereo VO pipeline by two and four times,
respectively.

本文提出了使用连续时间轨迹估计和带有物理启示的高斯过程回归，直接估计带有个体事件测量时间的完整立体视觉里程计流水线，避免了传统的分组或近似技术，并成功在 MVSEC 数据集上评估和验证，相对误差分别为 7.9e-3 和 5.9e-3，这比现有公开事件基立体视觉里程计流水线的性能高两倍和四倍。