Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency
Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency
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DOI：
10.1109/tro.2020.2964138
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发表时间：
2020-06-01
影响因子：
7.8
通讯作者：
Vela, Patricio A.
中科院分区：
文献类型：
--
作者：
Zhao, Yipu;Vela, Patricio A.
对最先进的视觉里程计/视觉同时定位与地图构建（VSLAM）系统的分析揭示了在平衡性能（准确性和鲁棒性）与效率（延迟）方面存在差距。基于特征的系统表现出良好的性能，但由于显式的数据关联而具有较高的延迟；直接和半直接系统延迟较低，但在某些目标场景中不适用，或者比基于特征的系统准确性更低。本文旨在通过对基于特征的VSLAM进行改进来填补性能 - 效率差距。我们提出了良好特征匹配，一种主动的地图到帧特征匹配方法。特征匹配工作与子矩阵选择相关，子矩阵选择具有组合时间复杂度，并且需要选择一种评分度量。通过模拟，显示最大对数行列式矩阵揭示度量表现最佳。为了实现实时适用性，研究了确定性选择和随机加速的组合。所提出的算法被集成到基于单目和双目特征的VSLAM系统中。在多个基准和计算硬件上进行的大量评估量化了延迟的降低以及准确性和鲁棒性的保持。
Analysis of state-of-the-art visual odometry/visual simultaneous localization and mapping (VSLAM) system exposes a gap in balancing performance (accuracy and robustness) and efficiency (latency). Feature-based systems exhibit good performance, yet have higher latency due to explicit data association; direct and semidirect systems have lower latency, but are inapplicable in some target scenarios or exhibit lower accuracy than feature-based ones. This article aims to fill the performance-efficiency gap with an enhancement applied to feature-based VSLAM. We present good feature matching, an active map-to-frame feature matching method. Feature matching effort is tied to submatrix selection, which has combinatorial time complexity and requires choosing a scoring metric. Via simulation, the Max-logDet matrix revealing metric is shown to perform best. For real-time applicability, the combination of deterministic selection and randomized acceleration is studied. The proposed algorithm is integrated into monocular and stereo feature-based VSLAM systems. Extensive evaluations on multiple benchmarks and compute hardware quantify the latency reduction and the accuracy and robustness preservation.