An EKF-based multiple data fusion for mobile robot indoor localization

Zhou, Guangbing; Luo, Jing; Xu, Shugong; Zhang, Shunqing; Meng, Shige; Xiang, Kui

doi:10.1108/aa-12-2020-0199

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…The LPF soothes the UWB data and reduces the effects of high-frequency noise and interference. Based on the filtered UWB data, the MVG, KF, and EKF algorithms are used to further reduce noise and interference effects and estimate position [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

Ranjan,

Shin,

Jung

et al. 2024

Sensors

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This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods—MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

Ranjan,

Shin,

Jung

et al. 2024

Sensors

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“…Therefore, the robot needs to determine by itself if the obtained pose is incorrect and then re-localize, which tests the localization failure recovery capabilities of the system. The commonly used robot localization methods include Markov localization and EKF localization [13,14], and AMCL [15][16][17].…”

Section: Localizationmentioning

confidence: 99%

“…However, since no other sensors sensing the external environment were used, the fused position could not be corrected, resulting in increasing the cumulative error of the position estimation system at a later stage. S. Julier combined UT and KF (Kalman Filter) and proposed the unscented Kalman filter (UKF) [16,33], which filters through the framework of the UT transform and Kalman filter algorithm to accomplish the optimal estimation of the target state with high accuracy of pose estimation.…”

Section: Pose Estimationmentioning

confidence: 99%

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

Ning,

Huang

2024

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The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose.

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“…Laser-SLAM is the positioning and navigation technologies in laser navigation systems that use LiDAR to perceive environmental information and perform simultaneous localization and mapping (Zhou et al , 2021). In the study of Zou et al (2022), different LiDAR SLAM-based indoor navigation methods were analyzed and compared, and extensive experiments were conducted to evaluate their performance in real-world environments.…”

Section: Literature Reviewmentioning

confidence: 99%

AGV robot for laser-SLAM based method testing in automated container terminal

Yang,

Cao,

Liu

et al. 2023

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Purpose Magnet spot is the primary method to develop the automated guided vehicle (AGV) guidance system for many automated container terminals (ACT). Aiming to improve the high flexibility of AGV operation in ACT, this paper aims to address the problem of technical stability leading to ACT production paralysis and propose a mini-terminal AGV robot for testing laser simultaneous location and mapping (SLAM)-based methods in ACT operation scenarios. Design/methodology/approach This study developed a physical simulation robot for terminal AGV operations, providing a platform to test technical solutions for applying laser navigation-related technologies in ACTs. Then, the terminal-AGV navigation system framework is designed to apply the laser-SLAM-based method in the physical simulation robot. Finally, the experiment is conducted in the terminal operation scenario to verify the feasibility of the proposed framework for lased-SLAM-based method testing and analyze the performance of the different mini-terminal AGV robots. Findings A series of experiments are conducted to analyze the performance of the proposed mini-terminal AGV robot for laser-SLAM-based method testing. The experimental results show the validity and effectiveness of the AGV robot and AGV navigation system framework with better local map matching, loopback and absolute positional error. Originality/value The proposed mini-terminal AGV robot and AGV navigation system framework can provide a platform for innovative laser-SLAM-based method testing in ACTs applications. Therefore, this study can effectively meet the high requirements of ACT for maturity and stability of the laser navigation technical.

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An EKF-based multiple data fusion for mobile robot indoor localization

Cited by 10 publications

References 36 publications

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

AGV robot for laser-SLAM based method testing in automated container terminal

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