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每个视觉变化，使其比传统帧相机对高速动态运动和弱光照环境更具鲁棒性。这也意味着场景中每次测量都可能发生在不同时刻，处理这些异时测量是事件相机应用的主要挑战。现有视觉里程计方法通常将时间邻近的测量近似为同一时刻事件，这种分组简化了估计问题，但牺牲了事件相机的本征时间分辨率（除非额外引入传感器）。本文提出完整的立体视觉里程计系统，直接利用各事件测量时刻进行估计，无需对估计状态进行任何分组或近似。通过采用物理先验的高斯过程回归，利用连续时间轨迹估计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，比现有公开事件立体视觉里程计系统性能提升2倍和4倍。