用隐式环境地图提升立体视觉惯导的长期定位精度。
SP-VINS: A Hybrid Stereo Visual Inertial Navigation System based on Implicit Environmental Map
- 用关键帧和2D特征点构建隐式地图，高效实现回环检测。
- 融合特征重投影与射线约束，统一更新测量残差。
- 在线标定相机-陀螺仪外参，适应光照变化环境。
基于滤波器的视觉惯性导航系统（VINS）因精度与效率平衡受移动机器人研究者青睐，但其映射质量限制了长期高精度状态估计。为此，本文提出一种新型基于滤波器的立体VINS，区别于传统依赖3D地图的SLAM系统，采用由关键帧和2D关键点构成的隐式环境地图，实现高效的回环闭合约束。其次，提出混合残差滤波框架，结合地标重投影与射线约束，构建统一雅可比矩阵以进行测量更新。最后，针对环境退化问题，将相机-惯性测量单元（IMU）外参融入视觉描述，实现在线标定。基准实验表明，所提SP-VINS在保持高计算效率的同时，实现了长期高精度定位性能，优于现有最先进方法。
原文摘要 · Abstract (English)
Filter-based visual inertial navigation system (VINS) has attracted mobile-robot researchers for the good balance between accuracy and efficiency, but its limited mapping quality hampers long-term high-accuracy state estimation. To this end, we first propose a novel filter-based stereo VINS, differing from traditional simultaneous localization and mapping (SLAM) systems based on 3D map, which performs efficient loop closure constraints with implicit environmental map composed of keyframes and 2D keypoints. Secondly, we proposed a hybrid residual filter framework that combines landmark reprojection and ray constraints to construct a unified Jacobian matrix for measurement updates. Finally, considering the degraded environment, we incorporated the camera-IMU extrinsic parameters into visual description to achieve online calibration. Benchmark experiments demonstrate that the proposed SP-VINS achieves high computational efficiency while maintaining long-term high-accuracy localization performance, and is superior to existing state-of-the-art (SOTA) methods.
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