Localization of Mobile Robot using Particle Filter

Woo, Jeong Su; Kim, Young-Joong; Lee, Jeong-on; Lim, Myo–Taeg

doi:10.1109/sice.2006.315151

Cited by 28 publications

(12 citation statements)

References 6 publications

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“…The previous success of PF was only limited to low-dimensional measurement problems such as localization of robot in free defined maps [25]. The authors presented a variety of approaches that are mainly focused on the localization of robots in various environments such as in [26], [27], a self-localization technique is presented for a mobile robot that is based on the PF in active beacon system. The technique estimates the value of a mobile robot position and heading by applying the ultrasonic sensor and PF is applied to eliminate the measurement and process noise.…”

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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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…The choice of EKF is motivated by the short computational time required (compared e.g. to particle filters [17], [18]). Assuming that humans move with constant velocity in a certain interval of time and that the system and measurement noises have Gaussian distribution, we can describe the human motion in polar coordinates by the following discrete state variable equations.…”

Section: B Person's State Estimationmentioning

confidence: 99%

A framework for the design of person following behaviors for social mobile robots

Granata

Bidaud

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents an environment for design of interactive behaviors for social robots. Its structure consists of three levels which are: perception, decision and action. Their principles are briefly described and we detail more specifically implementation of people searching and monitoring in cluttered environments. We describe in particular the methods for people detection, state estimation and trajectory generation for interactive monitoring. We demonstrate experimentally that the designed reasoning system is able to regulate the robot behavior in complex situations. The robustness of the framework is evaluated experimentally for person following in his home space that may lead the system to lose the person detection because of occlusions and to use shortcuts.

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“…복잡한 물리 공 간(physical space)을 돌아다니며 사람들을 위해 다양한 서 비스를 제공하는 지능형 에이전트인 이동 로봇의 경우도 작 업 공간내 자신이 현재 어디에 위치하는지를 추정해내는 일 은 다른 고수준의 작업 수행을 위해 기본적으로 요구되는 기능이면서도 실제로는 가장 어려운 일 중의 하나이다 [1,2]. 본 논문에서는 가장 효과적인 확률(probability) 기반의 측위 기법인 파티클 필터(Particle filter) [3,4] …”

Section: 서 론 1)unclassified

Particle Filter Localization Using Noisy Models

Kim¹,

Kim²,

Kim³

2012

The KIPS Transactions:PartB

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One of the most fundamental functions required for an intelligent agent is to estimate its current position based upon uncertain sensor data. In this paper, we explain the implementation of a robot localization system using Particle filters, which are the most effective one of the probabilistic localization methods, and then present the result of experiments for evaluating the performance of our system. Through conducting experiments to compare the effect of the noise-free model with that of the noisy state transition model considering inherent errors of robot actions, we show that it can help improve the performance of the Particle filter localization to apply a state transition model closely approximating the uncertainty of real robot actions.

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Localization of Mobile Robot using Particle Filter

Cited by 28 publications

References 6 publications

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

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

A framework for the design of person following behaviors for social mobile robots

Particle Filter Localization Using Noisy Models

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