多分辨率体素地图让边缘设备高效实现高精度视觉惯性定位
Multi-Resolution Voxelized Map-Based Stereo Visual-Inertial Odometry
- 用多分辨率体素化地图，每个体素只存一个点，减少数据量
- 通过锥形索引和DDA算法，实时匹配特征与地图点，定位误差小于5厘米
- 适合资源受限的无人机、机器人等边缘设备使用
将先验地图引入视觉惯性里程计（VIO）可显著提升位姿估计的精度与鲁棒性。然而，地图数据量大，加上传输带宽有限，难以持续将局部地图加载到边缘设备。本文提出一种多分辨率先验地图构建方法及配套的基于地图的VIO系统。地图在多个分辨率下体素化，每个体素仅保留一个地图点。在线运行时，采用锥形索引策略将边缘设备上的2D特征与3D地图点关联，锥体截面由当前位置到3D点的距离决定，从而选择合适分辨率，并通过3D数字微分分析（DDA）算法在对应体素中检索唯一地图点。该方法有效降低传输数据量与数据关联计算开销。在两个公开数据集上的大量实验表明，本系统在极低数据传输条件下仍能实现高精度位姿估计。
原文摘要 · Abstract (English)
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.
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