Angular velocity estimation based on adaptive simplified spherical simplex unscented Kalman filter in GFSINS

Wu, Qingya; Jia, Qingzhong; Shan, Jiayuan; Meng, Xiuyun

doi:10.1177/0954410013492255

Cited by 17 publications

(19 citation statements)

References 15 publications

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“…Second, in general, the rigid body transformation between the LiDAR and the IMU is unknown. Third, both signals are discrete and have different temporal resolutions, the LiDAR typically scanning information is available at 5–20 Hz or less, however the IMU produces data is normally available at 100 Hz or more [17,18,19,20]. …”

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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“…Due to the imprecision of suspension model and statistical characteristics of process noise, a suboptimal MAP noise estimator is employed to estimate the covariance matrix Q r of process noise in this study . if the estimated covariance matrix

{\overset{truê}{boldQ}}_{r, k}

is half‐positive, it satisfies

boldΓ {\overset{truê}{boldQ}}_{r, k + 1} Γ^{T} = ((), 1 - d_{k + 1}) boldΓ {\overset{truê}{boldQ}}_{r, k} Γ^{T} + d_{k + 1} true[G_{o, k + 1} Ω_{k + 1} {({boldG}_{o, k + 1} {boldΩ}_{k + 1})}^{T} + P_{x, k + 1} + \sum_{i = 1}^{n + 2} W_{i} ((), χ_{k + 1, i} - {\overset{truê}{boldx}}_{o, k + 1 / k}) {({boldχ}_{k + 1, i} - {\overset{x}{true}}_{o, k + 1 / k})}^{T} true]

in which the fading factor is d k + 1 = (1 − b )/(1 − b k + 2 ), the forgetting factor is b (0.95 <b< 0.99).…”

Section: Closed‐system Controller Designmentioning

confidence: 99%

Section: Suspension State Observer Designmentioning

confidence: 99%

“…In this paper, a spherical simplex UKF (SSUKF) based on the singular value decomposition (SVD) is adopted to solve these problems. The SSUKF is a non‐linear filter based on the spherical simplex unscented transformation . Compared to EKF, the SSUKF can improve the estimation performance with second‐order accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…And it also can reduce and avoid the computational complexity and instability caused by the large amounts and negative weights of the sigma sampling point in UKF, respectively. These prominent advantages have been reported in references [], but few studies have adopted the SSUKF observer for active suspension systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New SSUKF Observer for Sliding Mode Force Tracking H_∞ Control of Electrohydraulic Active Suspension

Zhang

Ding

Zhang

et al. 2018

Asian Journal of Control

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Compared to passive and semi‐active suspensions, active suspensions have the capacity to overcome the tradeoff design of vehicle ride comfort and handling performance. In this paper, a new control system with modified spherical simplex UKF (SSUKF) observer and sliding mode force tracker is proposed for electrohydraulic active suspensions. The estimated states obtained from the SSUKF observer are used to derive the mode energy of body motion and design the target demanded forces for suspension actuators. A sliding mode controller is presented to drive electrohydraulic actuators to track the demanded forces. Based on a robust H∞ control method, a new control strategy depending on body mode energy is proposed to restrict body motion. The performance comparisons between passive and active suspensions under three typical excitations are presented, and the obtained results indicate that the proposed control system can significantly depress body motion and decrease the mode energy to improve ride comfort and also effectively enhance the road hold abilities.

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Kalman filters based on multibody models: linking simulation and real world. A comprehensive review

et al. 2023

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The Kalman filter algorithm estimates variables of linear systems combining information from real sensors and a mathematical model of the system. It may be applied to observe nonlinear systems by means of a linearization of the system model. Multibody system dynamics constitutes a methodology for the analysis and design of mechanical systems. During the last twenty years, many ways of employing a multibody model as the Kalman filter model have been explored.This paper gathers up diverse algorithms, from the first ones based on the continuous expressions of the filter, to the indirect methods that enable real-time implementations of the observation of mechanical systems with a large number of variables. A detailed explanation of the methods and a description of the strengths and weaknesses of each one is presented along this paper, including a benchmark evaluating the performance of the methods.An important aspect of the Kalman filter is the characterization of the system uncertainty by means of white Gaussian noise. Sometimes, the statistical properties of the noise are unknown. Several methods to determine these properties are described, and a new methodology to model systems perturbed by colored noise (time-correlated noise) is presented.In Kalman filters based on multibody models, the information from a real mechanical system can be employed to keep the model behaving like the actual system with a great level of accuracy, linking the simulation to the real behavior of the system.

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Angular velocity estimation based on adaptive simplified spherical simplex unscented Kalman filter in GFSINS

Cited by 17 publications

References 15 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

A New SSUKF Observer for Sliding Mode Force Tracking H_∞ Control of Electrohydraulic Active Suspension

Kalman filters based on multibody models: linking simulation and real world. A comprehensive review

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Angular velocity estimation based on adaptive simplified spherical simplex unscented Kalman filter in GFSINS

Cited by 17 publications

References 15 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

A New SSUKF Observer for Sliding Mode Force Tracking H∞ Control of Electrohydraulic Active Suspension

Kalman filters based on multibody models: linking simulation and real world. A comprehensive review

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A New SSUKF Observer for Sliding Mode Force Tracking H_∞ Control of Electrohydraulic Active Suspension