The WHU Rolling Shutter Visual-Inertial Dataset

The vast majority of modern consumer cameras employ a rolling shutter (RS) mechanism which has a price and electronic advantage to global shutter (GS). However, in geometric computer vision applications such as visual simultaneous localization and mapping (VSLAM), performances of accuracy and robust...

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Veröffentlicht in:IEEE access 2020, Vol.8, p.50771-50779
Hauptverfasser: Cao, Like, Ling, Jie, Xiao, Xiaohui
Format: Artikel
Sprache:eng
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Zusammenfassung:The vast majority of modern consumer cameras employ a rolling shutter (RS) mechanism which has a price and electronic advantage to global shutter (GS). However, in geometric computer vision applications such as visual simultaneous localization and mapping (VSLAM), performances of accuracy and robustness are usually deteriorated due to the rolling shutter effect when using the RS cameras. This paper introduced the Wuhan University Rolling Shutter Visual-Inertial (WHU-RSVI) synthetic dataset for evaluating VSLAM and VI-SLAM (visual-inertial SLAM) methods in which RS cameras or IMU data are typically used. The proposed synthetic dataset contains RS images, time-synchronized GS images, inertial measurement unit (IMU) measurements, and accurate ground truth. It provides camera images with 640\times 480 resolution at 30 Hz and IMU measurements from 90 Hz to 14400 Hz. The cubic B-spline curves are used to model the motion of trajectories. Based on the known trajectories, an image of each pose can be rendered, and the corresponding IMU measurement model is then established. The dataset provides realistic images and IMU measurements by modeling the sensor noise in RGB and IMU data. Two trajectories with three sequences of different motion speeds (i. e., slow, medium and fast corresponding to different rolling shutter effects) are contained in the proposed dataset. Herein, the proposed dataset can be applied to compare the impact of different rolling shutter effects on a specific method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2978589