VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough

Min, Zhixiang; Dunn, Enrique

doi:10.48550/arxiv.2104.06800

Cited by 2 publications

(1 citation statement)

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“…Direct, or feature-based, approaches [16] utilize the descriptors representing an image as a set of features. Indirect, or image-based, approaches [17], in turn, process images as a whole instead of extracting features, for example, methods based on optical flow.…”

Section: Related Workmentioning

confidence: 99%

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

Savinykh¹,

Kurenkov²,

Kruzhkov³

et al. 2022

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%