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.

翻译：事件相机异步捕捉场景中单个视觉变化，使其在应对高速动态和弱光照条件时比传统帧相机更具鲁棒性，同时意味着场景中的每个测量值可能产生于唯一时间点。处理这些不同时间点的测量值是利用事件相机的核心挑战，现有视觉里程计（VO）方法通常将时间邻近的测量值近似为同一时刻发生以简化估计问题，但这牺牲了事件相机固有的时间分辨率。本文提出完整的立体视觉里程计框架，可直接利用单次事件测量时间进行估计，无需分组或近似处理。通过采用具有物理先验的高斯过程回归进行连续时间轨迹估计，该方法完整保留了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，性能较现有公开事件相机立体视觉里程计方法分别提升2倍和4倍。