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, absent additional sensors, 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 in the estimation state. 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.

翻译：事件相机异步捕捉场景中的单个视觉变化，使其在高度动态运动和弱光照条件下比传统帧相机更具鲁棒性。这也意味着场景中每次测量都可能发生在唯一时刻，处理这些不同测量时间是使用事件相机的主要挑战。在视觉里程计（VO）流程中，通常通过将时间上相近的测量近似为发生在同一时刻来应对这一挑战。这种分组简化了估计问题，但在缺乏额外传感器的情况下牺牲了事件相机的固有时间分辨率。本文提出了一种完整的立体VO流程，可直接利用单个事件测量时间进行估计，无需任何分组或近似处理。该方法采用连续时间轨迹估计，通过具有物理先验的高斯过程回归，保持事件相机的时间保真度和异步特性。在MVSEC数据集上的评估表明，该方法在两个独立序列上分别达到7.9e-3和5.9e-3的均方根相对误差，性能分别是现有公开事件立体VO流程的2倍和4倍。