Intelligent Vehicles Localization Approaches between Estimation and Information: A Review

Osman, Mostafa; Hussein, Ahmed; Al-Kaff, Abdulla

doi:10.1109/icves.2019.8906426

Cited by 12 publications

(11 citation statements)

References 28 publications

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“…We remark that, in general, the MHE problem can be defined for any noise distribution, that is, w and v. However, note that MHE becomes a least squares problem if and only if additive zero mean Gaussian noises are assumed (Osman et al, 2019b). Furthermore, the MHE deals directly with nonlinearity in the optimization problem without the need for linearization.…”

Section: Mhementioning

confidence: 99%

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Osman

Mehrez

Daoud

et al. 2021

Transactions of the Institute of Measurement and Control

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In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

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Section: Mhementioning

confidence: 99%

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Osman

Mehrez

Daoud

et al. 2021

Transactions of the Institute of Measurement and Control

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“…SLAM is an essential component in any autonomous mobile robot (AMR) or autonomous vehicle (AV). For this purpose, the authors in [23], [24] presented a SLAM-based localization algorithm to improve the performance of localization. A FastSLAM algorithm is proposed which consisted of an intelligent bat-inspired resampling whose iteration times can be adaptively tuned based on the filter diverging degree.…”

Section: Related Workmentioning

confidence: 99%

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

et al. 2020

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Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization. INDEX TERMS Extended Kalman filter, localization, particle filter, robot, unscented Kalman filter, wireless sensor networks.

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“…Finally, the end-effector pose feedback can be determined by the pose of the mobile robot determined through the use of a localization algorithm (Osman et al, 2019) and the forward kinematic equations of the robotic arm T.…”

Section: Mobile Manipulator Kinematic Modelmentioning

confidence: 99%

End-Effector Stabilization of a 10-DOF Mobile Manipulator using Nonlinear Model Predictive Control

Osman¹,

Mehrez²,

Yang³

et al. 2021

Preprint

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Motion control of mobile manipulators (a robotic arm mounted on a mobile base) can be challenging for complex tasks such as material and package handling. In this paper, a task-space stabilization controller based on Nonlinear Model Predictive Control (NMPC) is designed and implemented to a 10 Degrees of Freedom (DOF) mobile manipulator which consists of a 7-DOF robotic arm and a 3-DOF mobile base. The system model is based on kinematic models where the end-effector orientation is parameterized directly by a rotation matrix. The state and control constraints as well as singularity constraints are explicitly included in the NMPC formulation. The controller is tested using real-time simulations, which demonstrate high positioning accuracy with tractable computational cost.

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Intelligent Vehicles Localization Approaches between Estimation and Information: A Review

Cited by 12 publications

References 28 publications

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

End-Effector Stabilization of a 10-DOF Mobile Manipulator using Nonlinear Model Predictive Control

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