Visual-Inertial SLAM as Simple as A, B, VINS
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arXiv
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| Format: | Preprint |
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2024
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| author | Merrill, Nathaniel Huang, Guoquan |
| author_facet | Merrill, Nathaniel Huang, Guoquan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visual-Inertial SLAM as Simple as A, B, VINS Merrill, Nathaniel Huang, Guoquan Robotics 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. |
| title | Visual-Inertial SLAM as Simple as A, B, VINS |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.05969 |