Original Loop-closure Detection Algorithm for Monocular vSLAM
This addresses loop-closure detection for monocular vSLAM in robotics, particularly for drones, but appears incremental as it builds on existing methods.
The authors tackled loop-closure detection in monocular vSLAM by proposing an original algorithm that works with dense, semi-dense, and feature-based methods, resulting in more accurate mapping and real-time speed suitable for drone control.
Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays. In this work we study one of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure detection. We analyze the existing methods and propose original algorithm for loop-closure detection, which is suitable for dense, semi-dense and feature-based vSLAM methods. We evaluate the algorithm experimentally and show that it contribute to more accurate mapping while speeding up the monocular vSLAM pipeline to the extent the latter can be used in real-time for controlling small multi-rotor vehicle (drone).