ViViD++ : Vision for Visibility Dataset

Citations

WEB OF SCIENCE

61
Citations

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65

초록

In this letter, we present a dataset capturing diverse visual data formats that target varying luminance conditions. While RGB cameras provide nourishing and intuitive information, changes in lighting conditions potentially result in catastrophic failure for robotic applications based on vision sensors. Approaches overcoming illumination problems have included developing more robust algorithms or other types of visual sensors, such as thermal and event cameras. Despite the alternative sensors' potential, there still are few datasets with alternative vision sensors. Thus, we provided a dataset recorded from alternative vision sensors, by handheld or mounted on a car, repeatedly in the same space but in different conditions. We aim to acquire visible information from co-aligned alternative vision sensors. Our sensor system collects data more independently from visible light intensity by measuring the amount of infrared dissipation, depth by structured reflection, and instantaneous temporal changes in luminance. We provide these measurements along with inertial sensors and ground-truth for developing robust visual SLAM under poor illumination.

키워드

Data sets for SLAMdata sets for robotic visiondata sets for robot learningEVENT-CAMERA DATASETPLACE RECOGNITIONODOMETRY
제목
ViViD++ : Vision for Visibility Dataset
저자
Lee, Alex JunhoCho, YounggunShin, Young-sikKim, AyoungMyung, Hyun
DOI
10.1109/LRA.2022.3168335
발행일
2022-07
유형
Article
저널명
IEEE Robotics and Automation Letters
7
3
페이지
6282 ~ 6289