# Visual-Inertial SLAM as Simple as A, B, VINS

arXiv: https://arxiv.org/abs/2406.05969 · HTML: https://mlsanity.com/paper/2406.05969/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

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

- [Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model](https://mlsanity.com/paper/2605.17327/) (2026)
- [VIMD: Monocular Visual-Inertial Motion and Depth Estimation](https://mlsanity.com/paper/2509.19713/) (2025)

## Abstract (arXiv)

We present AB-VINS, a different kind of visual-inertial SLAM system. Unlike most popular VINS methods which only use hand-crafted techniques, AB-VINS makes use of three different deep neural networks. Instead of estimating sparse feature positions, AB-VINS only estimates the scale and bias parameters (a and b) of monocular depth maps, as well as other terms to correct the depth using multi-view information, which results in a compressed feature state. Despite being an optimization-based system, the front-end motion tracking thread of AB-VINS surpasses the efficiency of a state-of-the-art filtering-based method while also providing dense depth. When performing loop closures, standard keyframe-based SLAM systems need to relinearize a number of variables which is linear with respect to the number of keyframes. In contrast, the proposed AB-VINS can incorporate loop closures while only affecting a constant number of variables. This is thanks to a novel data structure called the memory tree, where keyframe poses are defined relative to each other rather than all in one global frame, allowing for all but a few states to be fixed. While AB-VINS might not be as accurate as state-of-the-art VINS algorithms, it is shown to be more robust.