无需迭代优化，快速准确初始化视觉惯性系统
An Efficient Closed-Form Solution to Full Visual-Inertial State Initialization
- 基于小旋转和匀速假设，直接求解全状态
- 误差比优化方法低10%-20%，初始化时间缩短4倍
- 适合对启动速度要求高的实时系统
本文提出一种闭式初始化方法，无需非线性优化即可恢复完整的视觉惯性状态。与依赖迭代求解的先前方法不同，本方法在小旋转和匀速假设下，保持公式紧凑并保留运动与惯性测量间的本质耦合，获得解析解，实现易实现、数值稳定。进一步提出基于可观测性的两阶段初始化方案，在精度与延迟间取得平衡。在EuRoC数据集上的大量实验验证了假设：相比优化方法，本方法初始化误差降低10%-20%，初始化窗口缩短4倍，计算成本降低5倍。
原文摘要 · Abstract (English)
In this letter, we present a closed-form initialization method that recovers the full visual-inertial state without nonlinear optimization. Unlike previous approaches that rely on iterative solvers, our formulation yields analytical, easy-to-implement, and numerically stable solutions for reliable start-up. Our method builds on small-rotation and constant-velocity approximations, which keep the formulation compact while preserving the essential coupling between motion and inertial measurements. We further propose an observability-driven, two-stage initialization scheme that balances accuracy with initialization latency. Extensive experiments on the EuRoC dataset validate our assumptions: our method achieves 10-20% lower initialization error than optimization-based approaches, while using 4x shorter initialization windows and reducing computational cost by 5x.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。