High-Precision Multicamera-Assisted Camera-IMU Calibration: Theory and Method

Fu, Bo; Han, Fuzhang; Wang, Yue; Jiao, Yanmei; Ding, Xiaqing; Tan, Qimeng; Chen, Lei; Wang, Minhang; Xiong, Rong

doi:10.1109/tim.2021.3051726

Cited by 31 publications

(7 citation statements)

References 37 publications

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“…However, due to the narrow field of view of the monocular camera, the calibration results will be limited, so Ref. [100] proposed adding multiple additional cameras to assist the calibration based on the monocular IMUcamera system, and combining a multi-camera visual-inertial state estimation algorithm (denoted as Mu-CI) was applied to the sensor platform to perform the calibration, achieving excellent calibration performance. The joint calibration framework of the IMU-camera, as a novel vision-based calibration technique, utilizes the information collected from the frame sequence to estimate the calibration parameters, but the complexity and constraints still pose great challenges to this scheme.…”

Section: Future Trendsmentioning

confidence: 99%

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Shang

et al. 2022

Micromachines

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A review of various calibration techniques of MEMS inertial sensors is presented in this paper. MEMS inertial sensors are subject to various sources of error, so it is essential to correct these errors through calibration techniques to improve the accuracy and reliability of these sensors. In this paper, we first briefly describe the main characteristics of MEMS inertial sensors and then discuss some common error sources and the establishment of error models. A systematic review of calibration methods for inertial sensors, including gyroscopes and accelerometers, is conducted. We summarize the calibration schemes into two general categories: autonomous and nonautonomous calibration. A comprehensive overview of the latest progress made in MEMS inertial sensor calibration technology is presented, and the current state of the art and development prospects of MEMS inertial sensor calibration are analyzed with the aim of providing a reference for the future development of calibration technology.

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Section: Future Trendsmentioning

confidence: 99%

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Shang

et al. 2022

Micromachines

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“…For height positioning model, there are systematic and computational model errors when estimating the height of clustered pod-peppers combined with depth information In case of our depth camera D435i, they include calibration error, 0-2% recording error for depth image, image distortion due to camera jitter, and estimation error incurred from formula (9). Scholars have attempted multiple highprecision-camera assisted shooting [26] and high-precision calibration algorithm [27] to minimize three-dimensional positioning errors.…”

Section: Discussionmentioning

confidence: 99%

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Zhang

Chen

2022

IEEE Access

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Traditionally height of end effector of pod pepper harvester is fixed, which induces it hardly adapt to growth height of clustered peppers. Firstly, aiming at the problems of small size and clustered growth of pepper fruits during identification task, an improved Faster R-CNN algorithm is proposed. On the one hand, strategies such as increasing the types and number of high-resolution anchors and using RoI Align instead of RoI Pooling are employed to improve the detection accuracy for tiny targets. On the other hand, ResNet+FPN instead of VGG16 and ResNet backbone structure is adopted as the low-level feature extractor, so extracting capability for small features can be enhanced effectively. Furthermore, to precisely locate the position of clustered peppers, a height calculation model combining the 2D image recognition results with its depth information is advanced. Comparative experiments show that the overall accuracy AP and AP50 of our method reach 75.79% and 87.30%, respectively. Compared with VGG16 feature extraction model, the two indicators are improved by 8.7% and 1.3%, respectively. The small target detection accuracy AP small is increased about 11.4%, with recall rate AR small increased up to 10.2%. The overall loss rate Loss is reduced by 4.7%, which manifests greatly improvement compared to YOLOv3 model. The detection time of a single frame reaches 42ms, which is slightly longer than that of YOLOv3 network, but it can still meet the real-time detection requirements of pepper harvester. In 3D location experiment, the average absolute height error of clustered peppers from the ground is 4.4mm, that accounts to the relative average error of 1.1%, thus suffices the adjustment error requirement of the end effector.

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“…The widely used calibration toolbox Kalibr [27] conducts offline estimation of Camera-IMU extrinsic parameters within the Maximum Likelihood Estimation framework. Recently, to achieve higher accuracy in the results of extrinsic calibration, Fu et al [28] introduced the use of multiple cameras and IMU sensors for calibration, aiming to achieve a smaller lower limit on the estimated covariance of extrinsic parameters. Wu et al [17], leveraging an industrial robot manipulator as carrier, utilized a global optimal solution to estimate the Camera-IMU extrinsic parameters.…”

Section: Related Workmentioning

confidence: 99%

“…This method exhibits robustness against uncertain IMU biases as the IMU is not time-integrated. Both methods [17,28] mentioned above demonstrated excellent results in experiments, but due to the complexity of experimental setups, they may pose challenges for generalization to common scenarios. The calibration method employed in this paper aims to achieve a balance between ease of operation, stability, and precision.…”

Section: Related Workmentioning

confidence: 99%

Camera-IMU extrinsic calibration method based on intermittent sampling and RANSAC optimization

Liang,

Cheng,

Tan

et al. 2024

Meas. Sci. Technol.

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Accurately calibrate the extrinsic parameters between a camera and an Inertial Measurement Unit (IMU) is a prerequisite for achieving sensor data fusion in visual-inertial navigation systems. Due to delays introduced by triggering, transmission, and other factors, the sampled times of the camera and IMU do not align with the system timestamps, leading to a decrease in the accuracy of extrinsic calibration. Existing calibration methods are unable to avoid the temporal misalignment between the camera and IMU. This paper introduces a novel intermittent calibration sampling method to address temporal misalignment challenges. The approach entails detecting motion-stillness transition points in the IMU data as keyframe segmentation criteria, thereby decoupling data acquisition from system timestamps and aligning it with the sensor's motion process. Subsequently, the robot hand-eye calibration method is applied to the extrinsic calibration of the Camera-IMU, combining the Random Sample Consensus (RANSAC) algorithm to filter the poses of the camera and IMU. This process achieves precise calibration of extrinsic parameters. Experimental validation shows that the algorithm proposed in this paper enhances calibration accuracy when compared to the widely used Kalibr Camera-IMU calibration toolbox. The rotational extrinsic parameter accuracy increased by 181.25%, and the translational extrinsic parameter accuracy improved by 168.33%. These findings provide effective technical means for precise measurement of Camera-IMU extrinsic parameters.

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High-Precision Multicamera-Assisted Camera-IMU Calibration: Theory and Method

Cited by 31 publications

References 37 publications

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Camera-IMU extrinsic calibration method based on intermittent sampling and RANSAC optimization

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