基于连续时间高斯过程回归实现原生时间分辨率的Event-based立体视觉里程计
Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression
AI总结：
本文提出一种无需分组或近似的Event-based立体视觉里程计流程，通过物理动机先验的高斯过程回归进行连续时间轨迹估计，保持事件相机时间保真度，在MVSEC数据集上误差显著优于现有公开方法。
摘要：
Event-based相机异步捕获场景中的单个视觉变化。这使得它们比传统的基于帧的相机对高动态运动和不良照明更具鲁棒性。这也意味着场景中的每次测量都可能发生在独特的时间点。处理这些不同的测量时间是使用Event-based相机的主要挑战。在视觉里程计（VO）流程中，通常通过将时间上接近的测量近似为在一个共同时间发生来解决。这种分组简化了估计问题，但在没有额外传感器的情况下，牺牲了Event-based相机固有的时间分辨率。本文提出了一种完整的立体VO流程，直接使用单个事件测量时间进行估计，在估计状态中不需要任何分组或近似。它通过具有物理动机先验的高斯过程回归进行连续时间轨迹估计，以保持Event-based相机的时间保真度和异步特性。在MVSEC数据集上评估了其性能，在两个独立序列上实现了7.9e-3和5.9e-3的RMS相对误差，分别比现有的公开Event-based立体VO流程性能高出两倍和四倍。
英文摘要：
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.
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