VITAMIN-E: VIsual Tracking and MappINg With Extremely Dense Feature Points
VITAMIN-E: VIsual Tracking and MappINg With Extremely Dense Feature Points
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DOI：
10.1109/cvpr.2019.00987
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Masashi Yokozuka;Shuji Oishi;S. Thompson;A. Banno
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文献类型：
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作者：
Masashi Yokozuka;Shuji Oishi;S. Thompson;A. Banno
在本文中，我们提出了一种称为“VITAMIN-E”的新颖的间接单目同时定位和建图（SLAM）算法，由于跟踪极其密集的特征点，该算法具有高度精确性和鲁棒性。典型的间接方法很难重建密集的几何形状，因为它们需要仔细选择特征点以进行精确匹配。与传统方法不同，所提出的方法使用基于主导流估计的跟踪局部曲率极值来处理大量特征点。由于这可能会导致束调整期间的计算成本较高，因此我们提出了一种称为“子空间牛顿法”的新颖优化技术，该技术通过部分更新变量来显着提高束调整的计算效率。我们同时从重建点生成网格并将它们合并为整个三维 (3D) 模型。 SLAM 基准 EuRoC 上的实验结果表明，所提出的方法在轨迹估计的准确性和鲁棒性方面均优于 DSO、ORB-SLAM 和 LSD-SLAM 等最先进的 SLAM 方法。该方法仅使用 CPU 即可实时生成密集特征点，从而同时生成非常详细的 3D 几何图形。
In this paper, we propose a novel indirect monocular simultaneous localization and mapping (SLAM) algorithm called "VITAMIN-E," which is highly accurate and robust as a result of tracking extremely dense feature points. Typical indirect methods have difficulty in reconstructing dense geometry because of their careful feature point selection for accurate matching. Unlike conventional methods, the proposed method processes an enormous number of feature points using the tracking local extrema of curvature based on dominant flow estimation. Because this may lead to high computational cost during bundle adjustment, we propose a novel optimization technique called the "subspace Newton's method" that significantly improves the computational efficiency of bundle adjustment by partially updating the variables. We concurrently generate meshes from the reconstructed points and merge them for an entire three-dimensional(3D) model. Experimental results on the SLAM benchmark EuRoC demonstrated that the proposed method outperformed state-of-the-art SLAM methods such as DSO, ORB-SLAM, and LSD-SLAM, both in terms of accuracy and robustness in trajectory estimation. The proposed method simultaneously generated significantly detailed 3D geometry as a result of the dense feature points in real time using only a CPU.