用体素地图过滤事件相机噪声，提升立体视觉惯性里程计精度
Event-based Stereo Visual-Inertial Odometry with Voxel Map
- 基于体素的点选择与管理，逐体素优化地图点
- 在三个公开数据集上精度和效率均优于现有方法
- 适合高动态范围、低延迟的机器人定位场景
事件相机以其高动态范围和极佳的时间分辨率著称，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高质量地图点的选取变得困难，而这直接影响状态估计的精度。为此，我们提出 Voxel-ESVIO，一种基于体素地图管理的事件相机立体视觉惯性里程计系统，可高效筛选出高质量3D点。具体而言，该方法采用基于体素的点选择与体素感知的点管理策略，实现每体素层面的地图点选择与更新优化。这些协同机制能够高效获取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大量实验表明，Voxel-ESVIO 在精度和计算效率方面均优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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