A Fuzzy-Innovation-Based Adaptive Kalman Filter for Enhanced Vehicle Positioning in Dense Urban Environments

Woo, Rinara; Yang, Eun Sun; Seo, Dae-Won

doi:10.3390/s19051142

Cited by 32 publications

(31 citation statements)

References 19 publications

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“…In this experiment, the input distance is obtained from an HCSR04 mounted on the left side of the robot body. The readable distance value of the sensor is initially converted directly to the error value by using Equation (9). Then this error value would be processed on membership classification as input.…”

Section: The Wall-following Robot Controlled By Flc Optimized By Psomentioning

confidence: 99%

“…The input is then used to decide the required amount of Pulse Width Modulation (PWM) for both the powered wheels. The use of Proportional-Integral-Derivative (PID) and Fuzzy Logic Controller (FLC) [7,8,9,10] can be suggested and recommended as the closedloop controller [11] suitable for processing the input and giving the feedback to the further decision process.…”

Section: Introductionmentioning

confidence: 99%

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An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

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Section: The Wall-following Robot Controlled By Flc Optimized By Psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

View full text Add to dashboard Cite

show abstract

“…Implementing high-level automated driving technology in on-road vehicles needs to address many cutting-edge issues. Among them, accurate sideslip angle and attitude are highly significant [2]. For example, image processing and feature recognition could be aided by the external pitch and roll angle of the vehicle body [3].…”

Section: Introductionmentioning

confidence: 99%

IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics

Liu

Xia

et al. 2019

Sensors

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The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.

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“…The real-time carrier-phase based differential GNSS technology can eliminate satellite orbit and block errors, tropospheric and ionospheric delays to achieve centimeter-level localization accuracy, i.e. RTK (Real Time Kinematic) technology [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

Liu

Wang³

et al. 2019

Remote Sensing

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Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather.

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A Fuzzy-Innovation-Based Adaptive Kalman Filter for Enhanced Vehicle Positioning in Dense Urban Environments

Cited by 32 publications

References 19 publications

An FLC-Pso Algorithm-Controlled Mobile Robot

An FLC-Pso Algorithm-Controlled Mobile Robot

IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics

A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

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