共现感知的二次分配用于同时定位与地图构建中的局部特征匹配

中文摘要 AI 辅助

局部特征匹配将两幅图像中的关键点关联为关键点对，是视觉同时定位与地图构建（Visual SLAM）的基础。最近邻（NN）搜索常用于关键点匹配，但当多个候选具有相似代价时，难以选择正确的关键点对。为提高匹配精度，本文提出一种考虑两个关键点对之间成对共现的关键点匹配方法。该关键点匹配被形式化为二次分配问题，这是一个NP难组合优化问题，难以在传统计算机上快速求解。近年来，伊辛机（Ising machine）已被开发为能够解决困难组合优化问题的计算设备。使用基于模拟分岔的伊辛机，所提方法在HPatches数据集上相比传统方法将匹配精度提高了约8个百分点。此外，我们将所提方法集成到ORB-SLAM3（一个具有代表性的学术视觉SLAM系统）中，在KITTI数据集中多个相同形状物体重复排列的场景下，绝对位姿误差（APE）改善了3.78倍，相对位姿误差（RPE）改善了2.85倍，而这些场景对原始ORB-SLAM3的精确自位姿估计具有挑战性。

英文摘要

Local feature matching, which associates keypoints in two images as keypoint pairs, is fundamental to Visual Simultaneous Localization and Mapping (Visual SLAM). Nearest Neighbor (NN) search is commonly used for keypoint matching, but it has difficulty selecting correct keypoint pairs when multiple candidates have similar costs. To improve matching accuracy, this paper proposes a keypoint matching method that considers the pairwise co-occurrence of two keypoint pairs. The keypoint matching is formulated as a quadratic assignment problem, which is an NP-hard combinatorial optimization problem, making it difficult to solve quickly on conventional computers. Recently, Ising machines have been developed as computing devices capable of solving hard combinatorial optimization problems. Using a simulated bifurcation based Ising machine, the proposed method improved matching accuracy by approximately 8 percentage points over a conventional method on the HPatches dataset. Furthermore, we integrated the proposed method into ORB-SLAM3, a representative academic Visual SLAM system, and achieved a 3.78-fold improvement in absolute pose error (APE) and a 2.85-fold improvement in relative pose error (RPE) on the KITTI dataset scenes where multiple same shape objects are repeatedly arranged, which are challenging for accurate self-pose estimation by the original ORB-SLAM3.

发表机构

• Corporate Laboratory, Toshiba Corporation, Japan（东芝公司企业实验室）

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