标题： 基于事件的立体视觉惯性里程计与体素地图 显示英文标题

标题： Event-based Stereo Visual-Inertial Odometry with Voxel Map

摘要： 事件相机以其高动态范围和卓越的时间分辨率而闻名，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得选择高质量的地图点变得复杂，这些地图点对状态估计的精度起着关键作用。 为了解决这一挑战，我们提出了Voxel-ESVIO，这是一种基于事件的立体视觉惯性里程计系统，它利用体素地图管理，可以高效地过滤出高质量的3D点。 具体来说，我们的方法利用基于体素的点选择和体素感知的点管理，在每个体素的基础上共同优化地图点的选择和更新。 这些协同策略能够高效地检索当前帧中具有最高观测似然性的抗噪地图点，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方面都优于最先进的方法。 显示英文摘要

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.