# Fast and Robust Initialization for Visual-Inertial SLAM

arXiv: https://arxiv.org/abs/1908.10653 · HTML: https://mlsanity.com/paper/1908.10653/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Learned Monocular Depth Priors in Visual-Inertial Initialization](https://mlsanity.com/paper/2204.09171/) (2022)
- [EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems](https://mlsanity.com/paper/2308.02670/) (2023)
- [Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model](https://mlsanity.com/paper/2605.17327/) (2026)

## Abstract (arXiv)

Visual-inertial SLAM (VI-SLAM) requires a good initial estimation of the initial velocity, orientation with respect to gravity and gyroscope and accelerometer biases. In this paper we build on the initialization method proposed by Martinelli and extended by Kaiser et al. , modifying it to be more general and efficient. We improve accuracy with several rounds of visual-inertial bundle adjustment, and robustify the method with novel observability and consensus tests, that discard erroneous solutions. Our results on the EuRoC dataset show that, while the original method produces scale errors up to 156%, our method is able to consistently initialize in less than two seconds with scale errors around 5%, which can be further reduced to less than 1% performing visual-inertial bundle adjustment after ten seconds.