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.

翻译：事件相机是一种有趣的视觉外感受传感器，其工作原理是对亮度变化而非绝对图像强度积分做出响应。得益于这种设计，该传感器在动态挑战和光照条件复杂的情况下表现出卓越性能。尽管基于事件的同步定位与建图仍是一个具有挑战性的问题，但近期多项研究指出该传感器非常适用于基于先验地图的跟踪任务。通过采用跨模态配准范式，可以在由传统传感器预先构建的高精度地图基础上，实现大范围光照和动态条件下的相机自运动跟踪。本文基于近期提出的基于事件的几何半稠密跟踪范式，提出引入惯性信号以增强估计鲁棒性。具体而言，新增信号为位姿初始化提供了强约束，并在窗口化多帧跟踪过程中起到正则化作用。实验表明，所提框架在挑战性光照条件下性能显著提升，同时降低了维持高动态序列稳定跟踪所需的事件中间表征配准频率。我们在多样化真实世界序列上开展评估，并将所提方法与不同运行频率的纯事件基准方法进行了对比分析。