基于事件的立体视觉-惯性里程计与体素地图
Event-based Stereo Visual-Inertial Odometry with Voxel Map
AI总结：
提出 Voxel-ESVIO 系统，通过基于体素的点选择与管理优化地图点更新，高效筛选抗噪高质量3D点，在三个公开基准上的准确性和计算效率均超越现有最先进方法。
摘要：
事件相机以其高动态范围和出色的时间分辨率著称，被认为是视觉里程计的重要传感器。然而，事件流中的固有噪声使高质量地图点的选择变得复杂，而这些地图点对状态估计的精度起决定性作用。为应对此挑战，我们提出 Voxel-ESVIO，一种利用体素地图管理的基于事件的立体视觉-惯性里程计系统，能高效筛选出高质量3D点。具体而言，该方法利用基于体素的点选择和体素感知的点管理，在逐个体素的基础上共同优化地图点的选择与更新。这些协同策略能够高效检索当前帧中观测似然最高的抗噪地图点，从而确保状态估计的准确性。在三个公开基准上的广泛评估表明，我们的 Voxel-ESVIO 在准确性和计算效率上均优于现有最先进方法。
英文摘要：
The event camera, renowned for its high dynamic range and exceptional temporal resolution, is recognized as an important sensor for visual odometry. However, the inherent noise in event streams complicates the selection of high-quality map points, which critically determine the precision of state estimation. To address this challenge, we propose Voxel-ESVIO, an event-based stereo visual-inertial odometry system that utilizes voxel map management, which efficiently filter out high-quality 3D points. Specifically, our methodology utilizes voxel-based point selection and voxel-aware point management to collectively optimize the selection and updating of map points on a per-voxel basis. These synergistic strategies enable the efficient retrieval of noise-resilient map points with the highest observation likelihood in current frames, thereby ensureing the state estimation accuracy. Extensive evaluations on three public benchmarks demonstrate that our Voxel-ESVIO outperforms state-of-the-art methods in both accuracy and computational efficiency.
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。
