A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network

Nguyen, Hong Ha; Zhou, Junhong; Kang, Hee-Jun

doi:10.1016/j.neucom.2014.03.085

Cited by 194 publications

(96 citation statements)

References 28 publications

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“…During the course of LiDAR-IMU calibration, neither the EKF algorithm nor AKF algorithm can avoid time delay calibration bias and filtering divergences. Initially, the correspondences between LiDAR and IMU measurements are usually unknown; as a result the relative time delay information between the LiDAR-IMU data flows cannot be computed directly [13,14,15,16]. The question is posed differently as follows: first, the measurement rate information of LiDAR and IMU cannot be directly compared.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

Liu

2017

Sensors

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The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

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Section: Introductionmentioning

confidence: 99%

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

Liu

2017

Sensors

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“…(6)- (9). In practice, the semivariogram curve can be obtained by selecting the best fitted model according to the distribution of the sample data.…”

Section: Quantitative Analysis Of Error Similaritymentioning

confidence: 99%

“…Many methods such as S-model [3] and complete and parametrically continuous (CPC) model [4] were proposed to solve this problem. Hayati [5] added a rotational parameter on the basis of D-H model and proposed modified D-H model, which is widely used in kinematic calibration by later researchers [6][7][8][9][10]. Besides, product of exponentials (POE) formula [11] was also used in the robot kinematic model to perform robot calibration [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Positional error similarity analysis for error compensation of industrial robots

Zeng

Tian

Liao

2016

Robotics and Computer-Integrated Manufacturing

115

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“…[7][8][9] Kinematic calibration is one of the most widely used methods to improve the positioning accuracy of robots. [10][11][12][13][14] The calibration includes four steps: system error modeling, error measurement, parameter identification and error compensation.…”

Section: Introductionmentioning

confidence: 99%

Error modeling and calibration of a 4ṞRR redundant positioning system

Sheng

Zhang

et al. 2017

AIP Advances

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Using the macro-micro combination positioning system for nanomanipulating can fulfill the requirements of large workspace, high precision and multi-degrees of freedom. As a macro part of the macro-micro combination positioning system, a redundantly actuated three degrees-of-freedom (DOF) parallel kinematic mechanism (4ṞRR) with a directly driven system is studied in this paper. Firstly, the error sensitivity of the 4ṞRR planar parallel mechanism is analyzed with global errors sensitive index (GESI) based on the error model of the positioning system. Then, a novel and practical calibration method combined with an error compensation strategy is proposed for the 4ṞRR positioning system. Finally, in order to verify the proposed method, a series of experiments are conducted with the laser measurement system in creditable conditions, and the data are illustrated for comparisons. The experimental results show that the positioning accuracy of the 4ṞRR positioning system is improved, and the performances of the end-effector are enhanced based on the proposed method.

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A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network

Cited by 194 publications

References 28 publications

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

Positional error similarity analysis for error compensation of industrial robots

Error modeling and calibration of a 4ṞRR redundant positioning system

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