Calibration and Noise Identification of a Rolling Shutter Camera and a Low-Cost Inertial Measurement Unit

Lee, Chang-Ryeol; Yoon, Ju Hong; Yoon, Kuk-Jin

doi:10.3390/s18072345

Cited by 7 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They showed that the SLAM system could improve the scale estimation by considering the RS effect. A study close to ours [13] presents a discretetime optimization approach to calibrate the spatiotemporal parameters of a RS camera-IMU system. The approach assumes constant IMU biases and linearly interpolates poses for rolling shutter observations from poses at discrete times.…”

Section: Related Workmentioning

confidence: 99%

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

Huai¹,

Zhuang²,

Yuan³

et al. 2021

Preprint

View full text Add to dashboard Cite

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 RScamera-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 • 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 1 .

show abstract

Section: Related Workmentioning

confidence: 99%

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

Huai¹,

Zhuang²,

Yuan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The calibration of rolling shutter sensors has also been addressed as an important research topic [ 2 , 18 , 19 , 20 ]. Lee et al [ 21 ] tried to fuse sensors of a low-cost IMU and a rolling shutter camera. For this, the authors presented a technique to estimate the calibration and noise parameters of a low-cost IMU and rolling shutter camera.…”

Section: Introductionmentioning

confidence: 99%

Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera

Kim

Bae

Kim

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper proposes a technique to estimate the distance between an object and a rolling shutter camera using a single image. The implementation of this technique uses the principle of the rolling shutter effect (RSE), a distortion within the rolling-shutter-type camera. The proposed technique has a mathematical strength compared to other single photo-based distance estimation methods that do not consider the geometric arrangement. The relationship between the distance and RSE angle was derived using the camera parameters (focal length, shutter speed, image size, etc.). Mathematical equations were derived for three different scenarios. The mathematical model was verified through experiments using a Nikon D750 and Nikkor 50 mm lens mounted on a car with varying speeds, object distances, and camera parameters. The results show that the mathematical model provides an accurate distance estimation of an object. The distance estimation error using the RSE due to the change in speed remained stable at approximately 10 cm. However, when the distance between the object and camera was more than 10 m, the estimated distance was sensitive to the RSE and the error increased dramatically.

show abstract

A high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations

Qiu

Wang

et al. 2020

Measurement

View full text Add to dashboard Cite

Calibration and Noise Identification of a Rolling Shutter Camera and a Low-Cost Inertial Measurement Unit

Cited by 7 publications

References 37 publications

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

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

Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera

A high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations

Contact Info

Product

Resources

About