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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.

Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency