Reproduce “Square-Root Inverse Filter-based GNSS-Visual-Inertial Navigation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Jun Hu, Xiaoming Lang, Feng Zhang, Yinian Mao, Guoquan Huang
SRI-GVINS is a tightly-coupled GNSS-Visual-Inertial Navigation System that integrates diverse raw GNSS measurements within a numerically stable square-root inverse filter framework. The system achieves improved accuracy, demonstrating an average position RMSE of 0.143m with double-differenced carrier phase and competitive computational efficiency, with an estimator time of 3.703ms on public datasets.
Key insights
- The Square-Root Inverse Sliding Window Filter (SRI-SWF) provides superior numerical stability and computational efficiency for complex sensor fusion algorithms.
- Deep integration of diverse raw GNSS measurements, particularly double-differenced carrier phase, significantly improves positioning accuracy to sub-meter and even centimeter levels.
- Online calibration of GNSS-IMU extrinsic parameters and a robust filter-based initialization strategy are critical for system robustness and mitigating real-world errors.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.