# Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System

arXiv: https://arxiv.org/abs/2108.07200 · HTML: https://mlsanity.com/paper/2108.07200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry](https://mlsanity.com/paper/1605.00475/) (2016)
- [Rolling-Shutter Modelling for Direct Visual-Inertial Odometry](https://mlsanity.com/paper/1911.01015/) (2019)
- [Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach](https://mlsanity.com/paper/2001.06175/) (2020)
- [Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras](https://mlsanity.com/paper/1701.05748/) (2017)
- [Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motion](https://mlsanity.com/paper/1805.02543/) (2018)
- [The Mobile AR Sensor Logger for Android and iOS Devices](https://mlsanity.com/paper/2001.00470/) (2019)
- [Spatiotemporal Calibration of Camera and 3D Laser Scanner](https://mlsanity.com/paper/2006.16081/) (2020)

## Built on by (most cited first, 4 of 4)

- [EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories](https://mlsanity.com/paper/2405.17278/) (2024)
- [Multi-Visual-Inertial System: Analysis, Calibration and Estimation](https://mlsanity.com/paper/2308.05303/) (2023)
- [eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems](https://mlsanity.com/paper/2509.05923/) (2025)
- [Rolling Shutter Camera Self-Calibration](https://mlsanity.com/paper/2608.01509/) (2026)

## 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.