A Low-Cost Dead Reckoning Navigation System for an AUV Using a Robust AHRS: Design and Experimental Analysis

Sabet, Mohammad Taghi; Daniali, Hamidreza Mohammadi; Fathi, Alireza; Alizadeh, Ebrahim

doi:10.1109/joe.2017.2769838

Cited by 42 publications

(20 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This algorithm finds the intersecting points between the vertical lines and horizontal lines from the semantic segmented lines of the parking spaces. The position of the vehicle was estimated by dead reckoning using a motion sensor [ 17 , 40 , 41 ]. Then, the vehicle was controlled by control inputs with Model Predictive Control (MPC) in the lateral [ 42 , 43 , 44 , 45 ] and longitudinal directions [ 46 , 47 ] separately.…”

Section: Experimental Resultsmentioning

confidence: 99%

Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

Lee

Lim

Sunwoo

2020

Sensors

View full text Add to dashboard Cite

In automated parking systems, a path planner generates a path to reach the vacant parking space detected by a perception system. To generate a safe parking path, accurate detection performance is required. However, the perception system always includes perception uncertainty, such as detection errors due to sensor noise and imperfect algorithms. If the parking path planner generates the parking path under uncertainty, problems may arise that cause the vehicle to collide due to the automated parking system. To avoid these problems, it is a challenging problem to generate the parking path from the erroneous parking space. To solve this conundrum, it is important to estimate the perception uncertainty and adapt the detection error in the planning process. This paper proposes a robust parking path planning that combines an error-adaptive sampling of generating possible path candidates with a utility-based method of making an optimal decision under uncertainty. By integrating the sampling-based method and the utility-based method, the proposed algorithm continuously generates an adaptable path considering the detection errors. As a result, the proposed algorithm ensures that the vehicle is safely located in the true position and orientation of the parking space under perception uncertainty.

show abstract

Section: Experimental Resultsmentioning

confidence: 99%

Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

Lee

Lim

Sunwoo

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Yet, here too, a motion model is employed, and directional angles induced by the water current are ignored. The need for data about the water current is acknowledged in [27], where a group of AUVs cooperates by sharing information about mismatches found from a water current forecast. Similarly, in [28], we tracked the location of a submerged node also using drifting information from nearby beacons.…”

Section: Approaches For Underwater Dead Reckoningmentioning

confidence: 99%

Dead Reckoning for Trajectory Estimation of Underwater Drifters under Water Currents †

Klein

Diamant

2020

JMSE

View full text Add to dashboard Cite

Between external position updates, the most useful technique for trajectory estimation of a submerged drifter is dead reckoning (DR). These devices drift with the water current to measure the current’s velocity or to observe physical phenomena. We focus on the specific but important case of when the drifter, due to its size and shape, experiences acceleration by the water current, an effect that must be taken into account during the DR. The force induced by the water current over the drifter is translated into a shift in the heading direction, thus creating a horizontal (sideslip) and a vertical (angle of attack) directional angles between the drifter’s moving direction and its body frame. In this paper, we extend and modify techniques used for pedestrian DR and propose PCA-DR: a principle component analysis-based DR algorithm to estimate the directional angles. Used for cases where the water current is significant such that its force induces acceleration over the drifter and used only for short time periods of a few seconds between navigation fixes, PCA-DR uses acceleration measurements only and does not assume knowledge of the drifter’s dynamics. Instead, as part of the DR process, PCA-DR estimates the directional angles induced by the water current. Compared to the traditional DR approach, our results demonstrate good navigation performance. A designated sea experiment demonstrates the applicability of PCA-DR in a realistic sea environment.

show abstract

“…Another researcher group, in [ 27 ], used underwater model-aided dead reckoning to improve EKF response. They calculated aided velocity using an identified surge dynamic model.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Shaukat

Ahmed

Iqbal

et al. 2021

Sensors

View full text Add to dashboard Cite

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

show abstract

A Low-Cost Dead Reckoning Navigation System for an AUV Using a Robust AHRS: Design and Experimental Analysis

Cited by 42 publications

References 36 publications

Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

Dead Reckoning for Trajectory Estimation of Underwater Drifters under Water Currents †

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Contact Info

Product

Resources

About