Adaptive particle filter for localization problem in service robotics

Heilig, Alexander; Mamaev, Ilshat; Hein, Björn; Malov, Dmitrii

doi:10.1051/matecconf/201816101004

Cited by 10 publications

(5 citation statements)

References 9 publications

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“… is the set of all states up to time step k. In [14] the associated importance weight of the particle is defined as follows:…”

Section: Particle Filtermentioning

confidence: 99%

Optimal State Estimation via Adaptive Fuzzy Particle Filter

Sąsiadek,

Bitlmal

2023

PAR

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Particle Filters (PF) accomplish nonlinear system estimation and have received high interest from numerous engineering domains over the past decade. The main problem of PF is to degenerate over time due to the loss of particle diversity. One of the essential causes of losing particle diversity is sample impoverishment (most of particle’s weights are insignificant) which affects the result from the particle depletion in the resampling stage and unsuitable prior information of process and measurement noise. To address this problem, a new Adaptive Fuzzy Particle Filter (AFPF) is used to improve the precision and efficiency of the state estimation results. The error in AFPF state is avoided from diverging by using Fuzzy logic. This method is called tuning weighting factor (α) as output membership function of fuzzy logic and input memberships function is the mean and the covariance of residual error. When the motion model is noisier than measurement, the performance of the proposed method (AFPF) is compared with the standard method (PF) at various particles number. The performance of the proposed method can be compared by keeping the noise level acceptable and convergence of the particle will be measured by the standard deviation. The simulation experiment findings are discussed and evaluated.

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“… is the set of all states up to time step k. In [14] the associated importance weight of the particle is defined as follows:…”

Section: Particle Filtermentioning

confidence: 99%

Optimal State Estimation via Adaptive Fuzzy Particle Filter

Sąsiadek,

Bitlmal

2023

PAR

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show abstract

“…In the above equations R k represent the covariance matrix, n a = 2n, and v = √ n + λ as can be seen in equation (10). After calculating the sigma points and time updates, the measurement update and measurement noise are calculated.…”

Section: A Localization Through Unscented Kalman Filtermentioning

confidence: 99%

“…PF was first introduced in 1999, [10] in a localization context by the name of Monte Carlo Localization (MCL). In this situation, PF proposed a probabilistic method on how to evaluate the robot state in a prior defined map.…”

Section: Introductionmentioning

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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“…The algorithm takes in consideration change in weight and change in standard deviation in order to cover kidnapping before and after convergence of the robot's pose and with independence to the success of the recovery. Heilig et al 25 proposed a statistical approach to the weighting step, taking in consideration present and past standard deviation of the particles, which are also used to detect the kidnapping event, being able to recover with the introduced adaptive resampling, where the number of particles and covariance change depending on the system's confidence about the robot's pose.…”

Section: Related Workmentioning

confidence: 99%

Robust Hybrid Interval-Probabilistic Approach for the Kidnapped Robot Problem

Neuland

Mantelli

Jaulin

et al. 2021

Int. J. Unc. Fuzz. Knowl. Based Syst.

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For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

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Adaptive particle filter for localization problem in service robotics

Cited by 10 publications

References 9 publications

Optimal State Estimation via Adaptive Fuzzy Particle Filter

Optimal State Estimation via Adaptive Fuzzy Particle Filter

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

Robust Hybrid Interval-Probabilistic Approach for the Kidnapped Robot Problem

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