DarkSLAM: 低照度環境での信頼性の高い動作を実現するGAN支援ビジュアルSLAM
DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions
GANによる前処理で暗所画像を明るく補正し、低照度でも高精度に動作するビジュアルSLAM手法を提案。暗いシーケンスでの追跡時間が従来の0.6%から25.1%に向上した。
著者: Alena Savinykh, Mikhail Kurenkov, Evgeny Kruzhkov, Evgeny Yudin, Andrei Potapov, Pavel Karpyshev, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
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.
