发表机构
Huazhong University of Science and Technology; AI Chip Center for Emerging Smart Systems (AC-CESS)（华中科技大学; 新兴智能系统人工智能芯片中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究视觉惯性里程计中姿态估计问题，提出多分辨率先验地图构建方法及基于地图的VIO系统，通过体素化地图、锥形索引策略和DDA算法，减少数据传输量与计算负载，实验证明该系统能在少数据传输下实现准确姿态估计。
AI 中文摘要
结合先验地图可显著提高视觉惯性里程计（VIO）中姿态估计的准确性和鲁棒性。然而，地图数据量庞大且传输带宽有限，使得在边缘设备上持续加载本地地图不切实际。本文提出一种多分辨率先验地图构建方法及相应的基于地图的VIO系统。先验地图在多个分辨率下进行体素化，每个体素仅保留一个地图点。在线VIO操作期间，采用锥形索引策略将边缘设备上的二维特征与三维地图点相关联。通过三维数字微分分析器（DDA）算法，根据当前位置到三维点的距离确定锥体截距，从而选择合适的分辨率级别并检索相应体素内的唯一地图点。该方法最小化了传输所需的数据量和数据关联期间的计算负载。在两个公共数据集上进行的大量实验表明，我们的系统在需要最少数据传输的情况下实现了准确的姿态估计。
英文摘要
Incorporating prior maps significantly enhances the accuracy and robustness of pose estimation in visual-inertial odometry (VIO). However, the large data volume of such maps, combined with limited transmission bandwidth, makes it impractical to continuously load local maps onto an edge device. In this paper, we propose a multi-resolution prior map construction method and a corresponding map-based VIO system. The prior map is voxelized at multiple resolutions, with each voxel retaining only a single map point. During online VIO operation, a cone-shaped indexing strategy associates 2D features on the edge device with 3D map points. The cone's intercept is determined by the distance from the current position to the 3D points, enabling the selection of the appropriate resolution level and the retrieval of the unique map point within the corresponding voxel via a 3D digital differential analyzer (DDA) algorithm. This approach minimizes both the volume of data required for transmission and the computational load during data association. Extensive experiments on two public datasets demonstrate that our system achieves accurate pose estimation while requiring minimal data transmission.
Comments9 pages, 4 figures, 6 tables. Accepted to appear in the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
