Abstract
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.
Abstract (translated)
事件相机是一种有趣的视觉外部感官传感器，它对亮度的变化做出反应，而不是对绝对图像强度进行整合。由于这种设计，传感器在具有挑战性动态和光照条件的情况下表现出卓越的性能。虽然基于事件的同时跟踪和映射仍然是一个具有挑战性的问题，但许多最近的工作指出，该传感器非常适合基于先验地图的跟踪。通过利用跨模态配准范式，相机的自运动可以在大量光照和动态条件下跟踪。 本文是对一种最近引入的事件基于几何半密度跟踪范式进行了延续，并提出了惯性信号以增强估计的提议。具体来说，添加的信号为姿态初始化以及窗口多帧跟踪期间的平滑化提供了强的提示。因此，所提出的框架在具有挑战性光照条件以及保持高度动态序列中稳定跟踪的情况下实现了更高的性能。我们的评估重点在一个多样化的真实世界序列上进行比较，包括与不同速率的纯事件跟踪算法的比较。
URL
https://arxiv.org/abs/2408.01370
