论文标题
良好的功能匹配：朝着低潜伏期的准确，强大的vo/vslam
Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
论文作者
论文摘要
对最先进的VO/VSLAM系统的分析暴露了平衡性能（准确性和鲁棒性）和效率（延迟）的差距。基于功能的系统表现出良好的性能，但由于显式数据关联而具有较高的延迟；直接和半程系统的延迟较低，但在某些目标方案中不适用或表现出比基于功能的准确性较低。本文旨在通过用于基于功能的VSLAM的增强功能来填补性能效率差距。我们提出了良好的功能匹配，这是一种主动地图到框架功能匹配方法。功能匹配工作与子正确选择相关，该选择具有组合时间的复杂性，需要选择评分度量。通过仿真，显示出最佳性能的max-logdet矩阵揭示度量。对于实时适用性，研究了确定性选择和随机加速的组合。所提出的算法已集成到基于单眼和立体特征的VSLAM系统中。对多个基准测试和计算硬件的广泛评估量化了降低的延迟和准确性和鲁棒性。
Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency). Feature-based systems exhibit good performance, yet have higher latency due to explicit data association; direct & semidirect systems have lower latency, but are inapplicable in some target scenarios or exhibit lower accuracy than feature-based ones. This paper 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 & stereo feature-based VSLAM systems. Extensive evaluations on multiple benchmarks and compute hardware quantify the latency reduction and the accuracy & robustness preservation.
