Visual Odometry (VO) and SLAM are fundamental components for spatial perception in mobile robots. Despite enormous progress in the field, current VO/SLAM systems are limited by their sensors' capability. Event cameras are novel visual sensors that offer advantages to overcome the limitations of standard cameras, enabling robots to expand their operating range to challenging scenarios, such as high-speed motion and high dynamic range illumination. We propose a novel event-based stereo VO system by combining two ideas: a correspondence-free mapping module that estimates depth by maximizing ray density fusion and a tracking module that estimates camera poses by maximizing edge-map alignment. We evaluate the system comprehensively on five real-world datasets, spanning a variety of camera types (manufacturers and spatial resolutions) and scenarios (driving, flying drone, hand-held, egocentric, etc). The quantitative and qualitative results demonstrate that our method outperforms the state of the art in majority of the test sequences by a margin, e.g., trajectory error reduction of 45% on RPG dataset, 61% on DSEC dataset, and 21% on TUM-VIE dataset. To benefit the community and foster research on event-based perception systems, we release the source code and results: https://github.com/tub-rip/ES-PTAM

本研究解决了现有视觉里程计（VO）和同时定位与地图构建（SLAM）系统在高动态范围和高速度环境下的局限性。提出了一种新颖的基于事件的立体视觉里程计系统，结合了无对应映射模块和最大化边缘图对齐的跟踪模块。实验结果显示，该方法在多数测试序列上明显优于现有技术，创造了显著的轨迹误差减少，提高了机器人在复杂环境下的空间感知能力。

基于事件的立体平行跟踪与地图构建（ES-PTAM）