Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can
output compact visual data based on a change in the intensity in each pixel
location asynchronously. In this paper, we study the application of current
image-based SLAM techniques to these novel sensors. To this end, the
information in adaptively selected event windows is processed to form
motion-compensated images. These images are then used to reconstruct the scene
and estimate the 6-DOF pose of the camera. We also propose an inertial version
of the event-only pipeline to assess its capabilities. We compare the results
of different configurations of the proposed algorithm against the ground truth
for sequences of two publicly available event datasets. We also compare the
results of the proposed event-inertial pipeline with the state-of-the-art and
show it can produce comparable or more accurate results provided the map
estimate is reliable.

本文介绍了使用适应性选择事件窗口中的信息形成运动补偿图像来重建场景以及估计相机的 6-DOF 姿态，并提出了一个惯性版本的仅事件流水线，以评估其性能。通过对两个公开事件数据集的序列的不同配置的结果与基准进行比较，本文证明了所提出的事件惯性流水线可以产生相当或更准确的结果，前提是地图估计可靠。