Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras

In this letter, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion-distorted rolling shutter images. To prevent intracta...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2022-10, Vol.7 (4), p.11537-11544
Main Authors: Lang, Xiaolei, Lv, Jiajun, Huang, Jianxin, Ma, Yukai, Liu, Yong, Zuo, Xingxing
Format: Article
Language:English
Subjects:
Calibration
Cameras
Continuous rolling
Delays
Localization
Robot vision systems
sensor fusion
Sliding
Splines (mathematics)
Trajectory
visual-inertial SLAM
Visualization
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Summary:In this letter, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion-distorted rolling shutter images. To prevent intractable computation load, the proposed VIO is sliding-window and keyframe-based. We propose to probabilistically marginalize the control points to keep the constant number of keyframes in the sliding window. Furthermore, the line exposure time difference (line delay) of the rolling shutter camera can be online calibrated in our continuous-time VIO. To extensively examine the performance of our continuous-time VIO, experiments are conducted on publicly-available WHU-RSVI, TUM-RSVI, and SenseTime-RSVI rolling shutter datasets. The results demonstrate the proposed continuous-time VIO significantly outperforms the existing state-of-the-art VIO methods.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3202349