ESVO2：基于立体事件相机的直接视觉-惯性里程计
ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras
AI总结：
针对事件相机直接法SLAM中建图计算复杂与旋转自由度退化问题，提出基于立体事件相机和IMU预积分的直接视觉-惯性里程计系统，提升建图效率与全局定位精度。
摘要：
基于事件的视觉里程计是视觉同步定位与建图（SLAM）技术的一个特定分支，旨在利用神经形态（即基于事件）相机的特殊工作原理，解决（通常并行的）跟踪与建图子问题。由于事件数据的运动依赖性，在大基线视点变化下建立显式数据关联（即特征匹配）较为困难，这使得直接方法成为更合理的选择。然而，最先进的直接方法受限于建图子问题的高计算复杂度以及相机位姿跟踪在旋转的某些自由度（DoF）上的退化问题。在本文中，我们通过在直接流水线之上构建基于事件的立体视觉-惯性里程计系统来解决这些问题。具体而言，为加速建图操作，我们提出了一种根据事件局部动态对轮廓点进行采样的高效策略。通过融合时间立体和静态立体结果，建图性能在结构完整性和局部平滑性方面也得到了提升。为避免相机位姿跟踪在恢复一般6自由度运动的俯仰和偏航分量时出现退化，我们通过预积分引入IMU测量作为运动先验。为此，我们设计了一个紧凑的后端，用于持续更新IMU偏置并预测线速度，从而为相机位姿跟踪提供准确的运动预测。所提出的系统能够很好地适应现代高分辨率事件相机，并在大规模室外环境中实现更好的全局定位精度。在五个具有不同分辨率和场景的公开数据集上进行的大量评估表明，所提出的系统相对于五种最先进方法具有优越性能。
英文摘要：
Event-based visual odometry is a specific branch of visual Simultaneous Localization and Mapping (SLAM) techniques, which aims at solving tracking and mapping subproblems (typically in parallel), by exploiting the special working principles of neuromorphic (i.e., event-based) cameras. Due to the motion-dependent nature of event data, explicit data association (i.e., feature matching) under large-baseline view-point changes is difficult to establish, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping sub-problem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this paper, we tackle these issues by building an event-based stereo visual-inertial odometry system on top of a direct pipeline. Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general 6-DoF motion, we introduce IMU measurements as motion priors via pre-integration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
