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 enabling continuous mapping for enhanced spatial resolution. 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。与依赖事件生成模型或对比度最大化的现有方法不同，EROAM通过将事件投影到单位球面上，采用球面事件表示，并引入专为事件相机数据设计的全新几何优化框架——事件球面迭代最近点（ES-ICP）。球面表示简化了旋转运动建模，同时支持连续建图以提升空间分辨率。结合并行点对线优化，EROAM在不牺牲精度的前提下实现了高效计算。在合成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。本方法在挑战性条件下（包括高角速度及长序列场景）保持稳定性能，而其他方法常出现失效或显著漂移。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。