Title: DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features,
Abstract: As the foundation of driverless vehicle and intelligent robots, Simultaneous
Localization and Mapping(SLAM) has attracted much attention these days.
However, non-geometric modules of traditional SLAM algorithms are limited by
data association tasks and have become a bottleneck preventing the development
of SLAM. To deal with such problems, many researchers seek to Deep Learning for
help. But most of these studies are limited to virtual datasets or specific
environments, and even sacrifice efficiency for accuracy. Thus, they are not
practical enough.
We propose DF-SLAM system that uses deep local feature descriptors obtained
by the neural network as a substitute for traditional hand-made features.
Experimental results demonstrate its improvements in efficiency and stability.
DF-SLAM outperforms popular traditional SLAM systems in various scenes,
including challenging scenes with intense illumination changes. Its versatility
and mobility fit well into the need for exploring new environments. Since we
adopt a shallow network to extract local descriptors and remain others the same
as original SLAM systems, our DF-SLAM can still run in real-time on GPU.