Visual-Inertial SLAM as Simple as A, B, VINS
<|reference_start|>Visual-Inertial SLAM as Simple as A, B, VINS: 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.<|reference_end|>
@article{merrill2024visual-inertial,
    title={Visual-Inertial SLAM as Simple as A, B, VINS},
    author={Nathaniel Merrill and Guoquan Huang},
    journal={arXiv preprint arXiv:2406.05969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05969},
    primaryClass={cs.RO}
}