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

Nathaniel Merrill; Guoquan Huang

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arXiv · 2406.05969

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

Nathaniel Merrill·Guoquan Huang

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Abstract

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.

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Nathaniel Merrill, Guoquan Huang. 2024-06-10. Visual-Inertial SLAM as Simple as A, B, VINS. https://arxiv.org/abs/2406.05969

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