IMU辅助的基于事件的立体视觉里程计
IMU-Aided Event-based Stereo Visual Odometry
AI总结：
本文改进基于事件的立体视觉里程计直接流程，通过事件局部动态的边缘像素采样、时序与静态立体融合及陀螺仪预积分先验，提升建图效率、完整性与6-DoF位姿跟踪精度，并开源系统。
摘要：
基于事件的视觉里程计的直接方法通过利用事件的生成模型建立隐式数据关联，从而求解建图和相机位姿跟踪子问题。该领域最新工作面临的主要瓶颈包括建图的高计算复杂度以及有限的跟踪精度。本文在准确性和效率方面改进了我们此前的直接流程 Event-based Stereo Visual Odometry。为加速建图操作，我们根据事件的局部动态提出了一种高效的边缘像素采样策略。通过融合时序立体结果与静态立体结果，建图在完整性和局部平滑性方面的性能也得到提升。为规避相机位姿跟踪在恢复一般6-DoF运动偏航分量时的退化问题，我们通过预积分引入陀螺仪测量作为先验。在公开数据集上的实验证明了我们的改进。我们将该流程作为开源软件发布，以支持该领域未来研究。
英文摘要：
Direct methods for event-based visual odometry solve the mapping and camera pose tracking sub-problems by establishing implicit data association in a way that the generative model of events is exploited. The main bottlenecks faced by state-of-the-art work in this field include the high computational complexity of mapping and the limited accuracy of tracking. In this paper, we improve our previous direct pipeline \textit{Event-based Stereo Visual Odometry} in terms of accuracy and efficiency. To speed up the mapping operation, we propose an efficient strategy of edge-pixel sampling according to the local dynamics of events. The mapping performance in terms of completeness and local smoothness is also improved by combining the temporal stereo results and the static stereo results. To circumvent the degeneracy issue of camera pose tracking in recovering the yaw component of general 6-DoF motion, we introduce as a prior the gyroscope measurements via pre-integration. Experiments on publicly available datasets justify our improvement. We release our pipeline as an open-source software for future research in this field.
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。
