IDOL：一个基于直线的IMU-DVS里程计框架
IDOL: A Framework for IMU-DVS Odometry using Lines
摘要：
在本文中，我们介绍了IDOL，一个基于优化的框架IMU-DVS里程使用线。事件摄像机，也称为动态视觉传感器（DVS），生成高度异步的事件流，触发每个像素的照明变化。这种新颖的范例在低光照条件和高速运动中具有优势。然而，这种非传统的传感方式带来了新的挑战，执行场景重建或运动估计。所提出的方法提供了利用惯性读数的连续时间表示将每个事件与及时准确的惯性数据相关联。该方法的前端提取属于环境中线段的事件集群，而后端通过最小化单个事件与图像空间中线条投影之间的点到线距离来估计线条3D位置旁边的系统轨迹。提出了一种新的吸引/排斥机制，以准确地估计线的端点，避免显式检测的事件数据。所提出的方法是基准对最先进的基于帧的视觉惯性里程框架使用公共数据集。结果表明，IDOL在大多数数据集上的表现都是相同的数量级，甚至表现出更好的方向估计。这些发现可能对DVS的新算法产生重大影响。
英文摘要：
In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sensing modality brings new challenges to perform scene reconstruction or motion estimation. The proposed method offers to leverage a continuous-time representation of the inertial readings to associate each event with timely accurate inertial data. The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space. A novel attraction/repulsion mechanism is presented to accurately estimate the lines' extremities, avoiding their explicit detection in the event data. The proposed method is benchmarked against a state-of-the-art frame-based visual-inertial odometry framework using public datasets. The results show that IDOL performs at the same order of magnitude on most datasets and even shows better orientation estimates. These findings can have a great impact on new algorithms for DVS.
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。
