Map-based Visual-Inertial Localization: A Numerical Study
Map-based Visual-Inertial Localization: A Numerical Study
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DOI：
10.1109/icra46639.2022.9811829
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Patrick Geneva;G. Huang
中科院分区：
文献类型：
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作者：
Patrick Geneva;G. Huang
我们重新讨论了在视觉惯性估计框架内有效利用先前地图信息的问题。对传统的基于地标的2D到3D测量地图和最近引入的基于关键帧的2D到2D测量地图的使用进行了研究。在视觉惯性模拟器中比较了先验MAP的完全联合估计与施密特-卡尔曼滤波(SKF)和测量膨胀方法在计算复杂性、一致性、精度和内存使用方面的差异。这项研究表明，SKF可以实现对小型工作空间场景的高效和一致的估计，并且使用2D到3D地标地图具有最高水平的准确性。基于关键帧的2D到2D贴图可以减少所需的状态大小，同时仍能提高精度。最后，我们证明，如果放松一致性保证，经过调整的测量膨胀方法对于大规模环境可以是准确和高效的。
We revisit the problem of efficiently leveraging prior map information within a visual-inertial estimation framework. The use of traditional landmark-based maps with 2D-to-3D measurements along with the recently introduced keyframe-based maps with 2D-to-2D measurements are inves-tigated. The full joint estimation of the prior map is compared within a visual-inertial simulator to the Schmidt-Kalman filter (SKF) and measurement inflation methods in terms of their computational complexity, consistency, accuracy, and memory usage. This study shows that the SKF can enable efficient and consistent estimation for small workspace scenarios and the use of 2D-to-3D landmark maps have the highest levels of accuracy. Keyframe-based 2D-to-2D maps can reduce the required state size while still enabling accuracy gains. Finally, we show that measurement inflation methods, after tuning, can be accurate and efficient for large-scale environments if the guarantee of consistency is relaxed.