Event cameras are bio-inspired visual sensors that capture pixel-wise intensity changes and output asynchronous event streams. They show great potential over conventional cameras to handle challenging scenarios in robotics and computer vision, such as high-speed and high dynamic range. This paper considers the problem of rotational motion estimation using event cameras. Several event-based rotation estimation methods have been developed in the past decade, but their performance has not been evaluated and compared under unified criteria yet. In addition, these prior works do not consider a global refinement step. To this end, we conduct a systematic study of this problem with two objectives in mind: summarizing previous works and presenting our own solution. First, we compare prior works both theoretically and experimentally. Second, we propose the first event-based rotation-only bundle adjustment (BA) approach. We formulate it leveraging the state-of-the-art Contrast Maximization (CMax) framework, which is principled and avoids the need to convert events into frames. Third, we use the proposed BA to build CMax-SLAM, the first event-based rotation-only SLAM system comprising a front-end and a back-end. Our BA is able to run both offline (trajectory smoothing) and online (CMax-SLAM back-end). To demonstrate the performance and versatility of our method, we present comprehensive experiments on synthetic and real-world datasets, including indoor, outdoor and space scenarios. We discuss the pitfalls of real-world evaluation and propose a proxy for the reprojection error as the figure of merit to evaluate event-based rotation BA methods. We release the source code and novel data sequences to benefit the community. We hope this work leads to a better understanding and fosters further research on event-based ego-motion estimation. Project page: https://github.com/tub-rip/cmax_slam

翻译：事件相机是一种仿生视觉传感器，能够捕捉像素级的强度变化并输出异步事件流。相较于传统相机，它们在处理机器人学和计算机视觉中的高速运动和高动态范围等挑战性场景时展现出巨大潜力。本文研究了利用事件相机进行旋转运动估计的问题。过去十年中，已开发出多种基于事件的旋转估计方法，但其性能尚未在统一标准下进行评估和比较。此外，这些先前工作未考虑全局优化步骤。为此，我们针对该问题进行了系统性研究，目标有两个：总结先前工作并给出我们自己的解决方案。首先，我们从理论和实验两方面对先前工作进行对比。其次，我们提出了首个基于事件的旋转专用光束平差（BA）方法。该方法借助最先进的对比最大化（CMax）框架构建，该框架具有原理性，无需将事件转换为帧。第三，我们利用所提出的BA构建了CMax-SLAM，这是首个包含前端和后端的基于事件的旋转专用SLAM系统。我们的BA既能离线运行（轨迹平滑），也能在线运行（CMax-SLAM后端）。为展示方法的性能和通用性，我们在合成数据集和真实世界数据集（包括室内、室外和太空场景）上进行了全面实验。我们讨论了真实世界评估中的陷阱，并提出用重投影误差的代理作为评价指标来评估基于事件的旋转BA方法。我们公开了源代码和新数据序列，以惠及学术社区。希望这项工作能加深对事件自运动估计的理解，并推动其进一步研究。项目页面：https://github.com/tub-rip/cmax_slam