EVIT：基于窗口化非线性优化的半稠密地图中事件视觉-惯性跟踪
EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization
AI总结：
本文提出EVIT方法，在事件相机半稠密地图跟踪中引入惯性信号进行位姿初始化和窗口化多帧正则化，提升挑战性光照下的跟踪性能并降低中间事件表示配准频率。
摘要：
事件相机是一种有趣的视觉外部感知传感器，它对亮度变化作出反应，而不是积分绝对图像强度。由于这种设计，该传感器在具有挑战性的动态和光照条件下表现出强大的性能。虽然基于事件的同时跟踪与建图仍然是一个具有挑战性的问题，但最近的一些工作已经指出该传感器适用于基于先验地图的跟踪。通过利用跨模态配准范式，可以在由更传统传感器事先创建的精确地图之上，在广泛的光照和动态条件下跟踪相机的自身运动。本文延续了最近引入的基于事件的几何半稠密跟踪范式，并提出加入惯性信号以增强估计的鲁棒性。具体而言，所添加的信号为位姿初始化以及窗口化多帧跟踪期间的正则化提供了强有力线索。因此，所提出的框架在具有挑战性的光照条件下实现了更高的性能，并且降低了中间事件表示需要配准的速率，以便在高度动态序列中保持稳定跟踪。我们的评估聚焦于一组多样化的真实世界序列，并将我们提出的方法与以不同速率运行的纯事件替代方案进行了比较。
英文摘要：
Event cameras are an interesting visual exteroceptive sensor that reacts to brightness changes rather than integrating absolute image intensities. Owing to this design, the sensor exhibits strong performance in situations of challenging dynamics and illumination conditions. While event-based simultaneous tracking and mapping remains a challenging problem, a number of recent works have pointed out the sensor's suitability for prior map-based tracking. By making use of cross-modal registration paradigms, the camera's ego-motion can be tracked across a large spectrum of illumination and dynamics conditions on top of accurate maps that have been created a priori by more traditional sensors. The present paper follows up on a recently introduced event-based geometric semi-dense tracking paradigm, and proposes the addition of inertial signals in order to robustify the estimation. More specifically, the added signals provide strong cues for pose initialization as well as regularization during windowed, multi-frame tracking. As a result, the proposed framework achieves increased performance under challenging illumination conditions as well as a reduction of the rate at which intermediate event representations need to be registered in order to maintain stable tracking across highly dynamic sequences. Our evaluation focuses on a diverse set of real world sequences and comprises a comparison of our proposed method against a purely event-based alternative running at different rates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
