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单个视觉变化，相较于传统帧式相机对高动态运动和弱光照具有更强的鲁棒性，这也意味着场景中每次测量均可在唯一时刻发生。处理这些不同时刻的测量是事件相机应用的主要挑战。现有视觉里程计（VO）方案通常通过将时间相近的测量近似为发生在同一时刻来解决该问题。这种分组简化了估计问题，但缺乏额外传感器时，会牺牲事件相机的固有瞬时分辨率。本文提出一种完整的立体VO系统，可直接利用单个事件测量时刻进行估计，无需在估计过程中进行任何分组或近似处理。该方法采用连续时间轨迹估计，通过基于物理先验的高斯过程回归保持事件相机的时间保真度与异步特性。在MVSEC数据集上的评估表明，该方法在两个独立序列上分别实现7.9e-3和5.9e-3的均方根相对误差，相比现有公开的事件立体VO系统性能分别提升2倍和4倍。