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

Qiu, Dongwei; Li, Shaofu; Wang, Tong; Ye, Qing; Li, Ruijie; Ding, Keliang; Hao, Xu

doi:10.1016/j.measurement.2019.107402

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…If low-cost IMU sensors are employed, measurement errors occur [ 56 ], making it difficult to correct camera movements. Therefore, it is necessary to use an expensive IMU sensor [ 39 ] or an additional device to fix the camera. However, in the proposed method, an additional device is not required because the measurement results are used to correct errors through a mathematical model.…”

Section: Discussionmentioning

confidence: 99%

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Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching and Anomaly Detection

Kim

Jeong

Lee³

et al. 2020

Sensors

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To prevent collapse accidents at construction sites, the marker-based displacement measurement method was developed. However, it has difficulty in obtaining accurate measurements at long distances (>50 m) in an outdoor environment because of camera movements. To overcome this problem, marker-based structural displacement measurement models using image matching and anomaly detection were designed in this study. Then, the performance of each model in terms of camera movement error correction was verified through comparison with that of a conventional model. The results show that the systematic errors due to camera movements (<1.7°) were corrected. The detection rate of markers with displacement reached 95%, and the probability that the error size would be less than 10 mm was ≥ 95% with a 95% confidence interval at a distance of more than 100 m. Moreover, the normalized mean square error was less than 0.1. The models developed in this study can measure the pure displacement of an object without the systematic errors caused by camera movements. Furthermore, these models can be used to measure the displacements of distant structures using closed-circuit television cameras and markers in an outdoor environment with high accuracy.

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Section: Discussionmentioning

confidence: 99%

“…Shichao et al [ 23 ] corrected the image motion error due to temperature by decomposing it into vectors. Recently, Qui et al [ 39 ] installed an inertial measurement unit (IMU) on a camera to infer the camera movement and performed a study on correcting the camera error.…”

Section: Introductionmentioning

confidence: 99%

Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching and Anomaly Detection

Kim

Jeong

Lee³

et al. 2020

Sensors

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“…IMU/camera calibration strategies can also be applied in other application areas [41] than those entailing UGVs or UAVs. It suggests that this approach can be considered a valid procedure, even if it seems uncommon.…”

Section: Related Workmentioning

confidence: 99%

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

Avola

Cinque

Foresti

et al. 2023

Sensors

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During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight.

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“…The monocular vision pose measurement based on control points (PnP issue) is to get the object pose relative to the camera coordinate system by collecting an image of n control points after calibrating the camera's intrinsic parameters [1,2]. Numerous fields, including advanced manufacturing [3], robotic hand-eye systems [4], biomedicine [5], visual simultaneous localization and mapping (SLAM) [6], etc, are among its many potential uses.…”

Section: Introductionmentioning

confidence: 99%

Camera calibration approach using polarized light based on high-frequency component variance weighted entropy for pose measurement

Zhang

et al. 2023

Meas. Sci. Technol.

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The camera’s intrinsic parameters calibration directly affects monocular pose measurement accuracy. To improve the calibration accuracy of the camera’s intrinsic parameters, the polarizing device is used to eliminate the high-brightness areas in the images collected for camera calibration. However, an unsuitable polarizing angle will reduce the image’s contrast. This causes the texture information of the image to be lost or makes the extracted feature change in the direction of grayscale value decline, ultimately affecting the feature extraction. Aiming at this issue, this paper proposed a camera calibration approach using polarized light based on high-frequency component variance weighted entropy. We determined the suitable polarizing angle according to the 2D homography matrix describing the mapping between the target plane and normalized imaging plane for each calibration location. Under the suitable polarizing angle, we determined the optimal exposure time according to the high-frequency component variance weighted entropy. The images collected under suitable polarizing angles and the optimal exposure time were used to finish the camera calibration. The suitable polarizing angles and optimal exposure time can ensure the image’s contrast and texture information. The experimental findings demonstrate that our approach can increase the camera calibration accuracy and thus pose measurement accuracy. In the -45° to +45° range, the average pose measurement error is less than 0.05° and lowered by 0.08° (62%) compared to that performed using the intrinsic parameters calibrated with the images collected under no polarizing device. In the 0 to 20 mm range, the average pose measurement error is less than 0.028 mm and lowered by 0.05 mm (64%) compared to that performed using the intrinsic parameters calibrated with the images collected under no polarizing device.

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

Cited by 15 publications

References 23 publications

Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching and Anomaly Detection

Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching and Anomaly Detection

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

Camera calibration approach using polarized light based on high-frequency component variance weighted entropy for pose measurement

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