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。

一种基于事件的算法，用于同时跟踪和绘制6自由度相机姿态