# An Efficient Closed-Form Solution to Full Visual-Inertial State Initialization

arXiv: https://arxiv.org/abs/2511.18910 · HTML: https://mlsanity.com/paper/2511.18910/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [VI3: Grounding Pretrained 3D Foundation Models with Inertial Cues](https://mlsanity.com/paper/2609.03824/) (2026)
- [DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping](https://mlsanity.com/paper/2609.27702/) (2026)

## Abstract (arXiv)

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.