论文标题
基于图像和事件融合的自我运动估计
Ego-motion Estimation Based on Fusion of Images and Events
论文作者
论文摘要
活动摄像机是一种新型的生物启发的视觉传感器，可输出事件流。在本文中，我们提出了一种称为EAS的新型数据融合算法，将常规强度图像与事件流融合。融合结果应用于某些自我运动估计框架，并在昏暗场景中获得的公共数据集上进行评估。在我们的3-DOF旋转估计框架中，EAS在强度图像和事件的表示中达到了最高的估计准确性，包括事件切片，TS和坐着。与原始图像相比，EAS将平均猿类减少了69％，从而受益于更多的跟踪功能。结果表明，我们的算法有效地利用了事件摄像机的高动态范围，以在困难照明条件下基于光流跟踪基于光流跟踪的自我运动估计框架的性能。
Event camera is a novel bio-inspired vision sensor that outputs event stream. In this paper, we propose a novel data fusion algorithm called EAS to fuse conventional intensity images with the event stream. The fusion result is applied to some ego-motion estimation frameworks, and is evaluated on a public dataset acquired in dim scenes. In our 3-DoF rotation estimation framework, EAS achieves the highest estimation accuracy among intensity images and representations of events including event slice, TS and SITS. Compared with original images, EAS reduces the average APE by 69%, benefiting from the inclusion of more features for tracking. The result shows that our algorithm effectively leverages the high dynamic range of event cameras to improve the performance of the ego-motion estimation framework based on optical flow tracking in difficult illumination conditions.
