EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time
This work addresses the challenge of accurate rotational odometry and mapping for event-based cameras, which is incremental by building on existing event-based approaches with a novel geometric optimization.
This paper tackles the problem of real-time camera rotation estimation using event-based cameras by introducing EROAM, a system that achieves higher accuracy, robustness, and computational efficiency compared to state-of-the-art methods, as demonstrated in experiments on synthetic and real-world datasets.
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.