Event cameras in motion tend to detect object boundaries or texture edges, which produce lines of brightness changes, especially in man-made environments. While lines can constitute a robust intermediate representation that is consistently observed, the sparse nature of lines may lead to drastic deterioration with minor estimation errors. Only a few previous works, often accompanied by additional sensors, utilize lines to compensate for the severe domain discrepancies of event sensors along with unpredictable noise characteristics. We propose a method that can stably extract tracks of varying appearances of lines using a clever algorithmic process that observes multiple representations from various time slices of events, compensating for potential adversaries within the event data. We then propose geometric cost functions that can refine the 3D line maps and camera poses, eliminating projective distortions and depth ambiguities. The 3D line maps are highly compact and can be equipped with our proposed cost function, which can be adapted for any observations that can detect and extract line structures or projections of them, including 3D point cloud maps or image observations. We demonstrate that our formulation is powerful enough to exhibit a significant performance boost in event-based mapping and pose refinement across diverse datasets, and can be flexibly applied to multimodal scenarios. Our results confirm that the proposed line-based formulation is a robust and effective approach for the practical deployment of event-based perceptual modules. Project page: https://gwangtak.github.io/roel/

翻译：运动中的事件相机倾向于检测物体边界或纹理边缘，这些边缘会产生亮度变化的线条，尤其是在人造环境中。虽然线条可以构成一种鲁棒的中间表示形式，且能被持续观测到，但线条的稀疏性可能导致在微小估计误差下性能急剧恶化。仅有少数先前工作（通常需借助额外传感器）利用线条来补偿事件传感器严重的领域差异以及不可预测的噪声特性。我们提出一种方法，能够通过一种巧妙的算法过程稳定地提取不同外观线条的轨迹，该过程观测来自事件多个时间切片的多重表示，从而补偿事件数据中潜在的干扰因素。随后，我们提出能够优化三维线地图与相机位姿的几何代价函数，以消除投影畸变和深度歧义。三维线地图具有高度紧凑性，并可配备我们提出的代价函数；该函数可适配于任何能够检测并提取线条结构或其投影的观测数据，包括三维点云地图或图像观测。我们证明，所提公式足够强大，能够在多样数据集上显著提升基于事件的地图构建与位姿优化性能，并可灵活应用于多模态场景。实验结果证实，所提出的基于线条的公式是一种鲁棒且有效的方法，可用于事件感知模块的实际部署。项目页面：https://gwangtak.github.io/roel/