Study on Installation Error Analysis and Calibration of Acoustic Transceiver Array Based on SINS/USBL Integrated System

Tong, Jinwu; Xu, Xiaosu; Zhang, Tao; Zhang, Liang; Yao, Li

doi:10.1109/access.2018.2878756

Cited by 42 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our work, the state of the motion model depends on the specific AUV, which may include the position, attitude and configuration of the manipulators of the I-AUV. Detailed definitions of an AUV models are presented in section V. For the observation model, we assume that visual sensors, underwater-GPS (USBL), IMU, pressure sensors and angle encoders are mounted at the appropriate positions of the robot, which provide the measurement of the base position, attitude and manipulator configuration [5], [6].…”

Section: Problem Definition a Continuous-time System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Existing belief space motion planning methods are not efficient for underwater robots that are subject to spatially varying motion and sensing uncertainties arising from the non-uniform current disturbances and landmark populations, respectively. Based on a closed-loop stochastic control framework, we propose a fast Gaussian belief space planning approach for coupled optimization of trajectory, localization and control, resulting in a non-linear programming problem (NLP). In particular, as opposed to advancing the covariance by a Kalman filter in the existing literature, we utilize an upper bound of the trace propagation of the covariance, thereby avoiding to solve Riccati equations and thus, reducing the computational complexity. The NLP is then efficiently solved by sequential quadratic programming based on the initial solutions obtained from RRT-connect. These initials lie in multiple homotopy classes guaranteed by H-signature discrimination, leading to global optimality with probability one as the number of samples in RRT-connect goes to infinity. Numerical simulations on holonomic and non-holonomic autonomous underwater vehicles (AUVs) and an Intervention-AUV with a manipulator in cluttered underwater environments demonstrate that optimal and collision-free trajectories with low localization uncertainty are obtained efficiently.

show abstract

Section: Problem Definition a Continuous-time System Modelmentioning

confidence: 99%

“…ROVs [5], [6]. In addition to autonomously moving in various underwater environments, Intervention-AUVs (I-AUVs) equipped with multi-degree of freedom (DOF) manipulators can perform complex intervention tasks such as construction, rescue and salvage [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In [ 17 ], a rotation test was utilized to enhance the observability of KF in order to calibrate the IMU biases. However, KF is an unbiased estimation method based on the assumption of GWN while IMU sensors noise is irregular [ 18 , 19 , 20 ]. In [ 21 ], an autocovariance least-squares (ALS) technique was proposed to estimate the process noise of IMU.…”

Section: Introductionmentioning

confidence: 99%

An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm

Wen

Yang

Cai

2021

Sensors

View full text Add to dashboard Cite

In the field of high accuracy strapdown inertial navigation system (SINS), the inertial measurement unit (IMU) biases can severely affect the navigation accuracy. Traditionally we use Kalman filter (KF) to estimate those biases. However, KF is an unbiased estimation method based on the assumption of Gaussian white noise (GWN) while IMU sensors noise is irregular. Kalman filtering will no longer be accurate when the sensor’s noise is irregular. In order to obtain the optimal solution of the IMU biases, this paper proposes a novel method for the calibration of IMU biases utilizing the KF-based AdaGrad algorithm to solve this problem. Three improvements were made as the following: (1) The adaptive subgradient method (AdaGrad) is proposed to overcome the difficulty of setting step size. (2) A KF-based AdaGrad numerical function is derived and (3) a KF-based AdaGrad calibration algorithm is proposed in this paper. Experimental results show that the method proposed in this paper can effectively improve the accuracy of IMU biases in both static tests and car-mounted field tests.

show abstract

“…However, SINS cannot be calibrated without reference information and will have error accumulation during the navigation process [3,4]. e performance of SINS highly depends on the accuracy of the initial navigation parameters, among which the initial velocity and position can be easily obtained from external references such as GPS and DVL [5,6]. But normally, the initial attitude needs to be obtained through the initial alignment process by the system, which becomes the main factor that influences the navigation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fast SINS Initial Alignment Method Based on Iterative Algorithms in Inertial Frame

Ding

Yao

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Iterative algorithms based on saved data have been researched for strapdown inertial navigation system to make the best use of sensor data, and different alignment algorithms can be carried out during the iterative process to fulfil initial alignment. This paper firstly analysed two interactive processes. The forward-backward process needs to change the normal system model in the backward process and have additional error factors. While the forward-forward process is easy to be carried out without changing the system model, the transition error will also decrease the accuracy. In addition, traditional iterative methods always need to save a large amount of data and do redundant computation, which is not highly effective, especially in view of the real-time property. To address these effects, a forward-forward solution in the inertial frame based on Kalman filter is analysed. This method calculates the attitude transfer matrix in the inertial frame to deal with angular disturbances and avoids saving all the original measurement data during the process, which makes the method more stable and faster to fulfil in the real-time system. Simulation and turntable test validates the performance of the proposed method.

show abstract

Study on Installation Error Analysis and Calibration of Acoustic Transceiver Array Based on SINS/USBL Integrated System

Cited by 42 publications

References 14 publications

Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm

Fast SINS Initial Alignment Method Based on Iterative Algorithms in Inertial Frame

Contact Info

Product

Resources

About