Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System
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Abstract (arXiv)
The rolling shutter (RS) mechanism is widely used by consumer-grade cameras, which are essential parts in smartphones and autonomous vehicles. The RS effect leads to image distortion upon relative motion between a camera and the scene. This effect needs to be considered in video stabilization, structure from motion, and vision-aided odometry, for which recent studies have improved earlier global shutter (GS) methods by accounting for the RS effect. However, it is still unclear how the RS affects spatiotemporal calibration of the camera in a sensor assembly, which is crucial to good performance in aforementioned applications. This work takes the camera-IMU system as an example and looks into the RS effect on its spatiotemporal calibration. To this end, we develop a calibration method for a RS-camera-IMU system with continuous-time B-splines by using a calibration target. Unlike in calibrating GS cameras, every observation of a landmark on the target has a unique camera pose fitted by continuous-time B-splines. With simulated data generated from four sets of public calibration data, we show that RS can noticeably affect the extrinsic parameters, causing errors about 1^circ in orientation and 2 cm in translation with a RS setting as in common smartphone cameras. With real data collected by two industrial camera-IMU systems, we find that considering the RS effect gives more accurate and consistent spatiotemporal calibration. Moreover, our method also accurately calibrates the inter-line delay of the RS. The code for simulation and calibration is publicly available.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 16 Aug 2021.
Builds on
10 of 30 references.
References · 10
- 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
- 2018The Double Sphere Camera Modelcited by 50
- 2019Efficient Derivative Computation for Cumulative B-Splines on Lie Groupscited by 28
- 2016Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometrycited by 26
- 2019Rolling-Shutter Modelling for Direct Visual-Inertial Odometrycited by 15
- 2020Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approachcited by 15
- 2017Robust Intrinsic and Extrinsic Calibration of RGB-D Camerascited by 7
- 2018Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motioncited by 5
- 2019The Mobile AR Sensor Logger for Android and iOS Devicescited by 2
- 2020Spatiotemporal Calibration of Camera and 3D Laser Scannercited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2024EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectoriescited by 5
- 2023Multi-Visual-Inertial System: Analysis, Calibration and Estimationcited by 1
- 2025eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systemscited by 0
- 2026Rolling Shutter Camera Self-Calibrationcited by 0
- 2026Rolling Shutter Camera Self-Calibration2 Aug 2026
- 2025eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems7 Sep 2025
- 2024EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories27 May 2024
- 2023Multi-Visual-Inertial System: Analysis, Calibration and Estimation10 Aug 2023