This paper presents EROAM, a novel event-based rotational odometry and mapping system that achieves real-time, accurate camera rotation estimation. Unlike existing approaches that rely on event generation models or contrast maximization, EROAM employs a spherical event representation by projecting events onto a unit sphere and introduces Event Spherical Iterative Closest Point (ES-ICP), a novel geometric optimization framework designed specifically for event camera data. The spherical representation simplifies rotational motion formulation while operating in a continuous spherical domain, enabling enhanced spatial resolution. Our system features an efficient map management approach using incremental k-d tree structures and intelligent regional density control, ensuring optimal computational performance during long-term operation. Combined with parallel point-to-line optimization, EROAM achieves efficient computation without compromising accuracy. Extensive experiments on both synthetic and real-world datasets show that EROAM significantly outperforms state-of-the-art methods in terms of accuracy, robustness, and computational efficiency. Our method maintains consistent performance under challenging conditions, including high angular velocities and extended sequences, where other methods often fail or show significant drift. Additionally, EROAM produces high-quality panoramic reconstructions with preserved fine structural details.

翻译：本文提出EROAM，一种新颖的基于事件的旋转里程计与建图系统，能够实现实时、精确的相机旋转估计。与现有依赖事件生成模型或对比度最大化的方法不同，EROAM采用球面事件表示方法，将事件投影至单位球面，并提出了事件球面迭代最近点（ES-ICP）——一种专为事件相机数据设计的新型几何优化框架。球面表示在连续球域中操作，简化了旋转运动建模，同时提升了空间分辨率。本系统采用基于增量k-d树的高效地图管理方法及智能区域密度控制，确保长期运行时的最优计算性能。结合并行点对线优化，EROAM在保持精度的同时实现了高效计算。在合成数据集与真实数据集上的大量实验表明，EROAM在精度、鲁棒性和计算效率方面显著优于现有先进方法。在高角速度、长序列等现有方法常失效或产生显著漂移的挑战性条件下，本方法仍能保持稳定的性能。此外，EROAM能够生成保留精细结构细节的高质量全景重建结果。