  Existing visual SLAM approaches are sensitive to illumination, with their
precision drastically falling in dark conditions due to feature extractor
limitations. The algorithms currently used to overcome this issue are not able
to provide reliable results due to poor performance and noisiness, and the
localization quality in dark conditions is still insufficient for practical
use. In this paper, we present a novel SLAM method capable of working in low
light using Generative Adversarial Network (GAN) preprocessing module to
enhance the light conditions on input images, thus improving the localization
robustness. The proposed algorithm was evaluated on a custom indoor dataset
consisting of 14 sequences with varying illumination levels and ground truth
data collected using a motion capture system. According to the experimental
results, the reliability of the proposed approach remains high even in
extremely low light conditions, providing 25.1% tracking time on darkest
sequences, whereas existing approaches achieve tracking only 0.6% of the
sequence time.
