Mobile robot localization via EKF and UKF: A comparison based on real data

D’Alfonso, Luigi; Lúcia, Walter; Muraca, P.; Pugliese, Paolo

doi:10.1016/j.robot.2015.07.007

Cited by 59 publications

(26 citation statements)

References 18 publications

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“…Thus, from Eq. (25) we can infer that y(t + 1) ∞ = 0, which in turn leads to lim k→∞ x k = 0 N , and completes the proof. However, we can show that a strict upper bound on the lengths of all or an infinite subset of slices is not necessary.…”

Section: A Asymptotic Behaviorsupporting

confidence: 54%

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An Opportunistic Linear–Convex Algorithm for Localization in Mobile Robot Networks

Safavi

Khan

2017

IEEE Trans. Robot.

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In this paper, we develop a distributed algorithm to localize a network of robots moving arbitrarily in a bounded region. In the case of such mobile networks, the main challenge is that the robots may not be able to find nearby robots to implement a distributed algorithm. We address this issue by providing an opportunistic algorithm that only implements a location update when there are nearby robots and does not update otherwise. We assume that each robot measures a noisy version of its motion and the distances to the nearby robots.To localize a network of mobile robots in R m , we provide a simple linear update, which is based on barycentric coordinates and is linear-convex. We abstract the corresponding localization algorithm as a Linear Time-Varying (LTV) system and show that it asymptotically converges to the true locations of the robots.We first focus on the noiseless case, where the distance and motion vectors are known (measured) perfectly, and provide sufficient conditions on the convergence of the algorithm. We then evaluate the performance of the algorithm in the presence of noise and provide modifications to counter the undesirable effects of noise. We further show that our algorithm precisely tracks a mobile network as long as there is at least one known beacon (a node whose location is perfectly known). † The authors are with the

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Section: A Asymptotic Behaviorsupporting

confidence: 54%

“…Robot localization approaches include but are not limited to dead-reckoning, [7]- [9], Simultaneous Localization and Mapping (SLAM), [10]- [14], Monte Carlo techniques [3], [5], [6], [15], [16], and Kalman Filtering methods [17]- [25]; other related works include [26]- [38]. We briefly describe the related work below.…”

Section: Introductionmentioning

confidence: 99%

An Opportunistic Linear–Convex Algorithm for Localization in Mobile Robot Networks

Safavi

Khan

2017

IEEE Trans. Robot.

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“….In [3]the  =90°have been discussed in this paper, we discuss the  in [0°,180°].We can approximate the…”

Section: Measurement Modelmentioning

confidence: 99%

“…This method using the data obtained from the mobile sensor like encoder to estimate the change of the position of mobile robot with time. However, in the process of localization of mobile robot, it has a inevitable problem of accumulative error when the mobile robot moves long distances [3][4].To improve the accuracy of odometry using in localization estimation of mobile robot, many research have been undergone in the filed of eliminate the systematic error, robot design constraints and environment influences. A common approach consists of two parts.…”

Section: Introductionmentioning

confidence: 99%

Research on EKF-Based Localization Method of Tracked Mobile Robot

Qu¹,

Zhang²,

Hou³

et al. 2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. To estimate the position and heading angle of mobile robot precisely, an measurement variable estimation model was proposed to adapt any angle. Fusing the predictive value of odometry and measurement data of multiple sensors by the Extended Kalman Filtering (EKF) for reducing the accumulative error by using only traditional odometry. The proposed models is verified by Matlab simulation and experimental results.

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“…Here the local form of the problem is considered, where the aim is to compensate for odometry errors which occur during robot navigation. This problem has been previously approached with the standard forms of the EKF and UKF [18].…”

Section: Application To Mobile Robot Localizationmentioning

confidence: 99%

A novel measurement processing approach to the parallel expectation propagation unscented Kalman filter

Freitas

Fritsche

Mihaylova

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. The focus is on applications consisting of a massive amount of data. From a modelling perspective, this amounts to a measurement vector with dimensionality significantly greater than the dimensionality of the state vector. The efficiency of the filter is derived from a parallel filter structure which is enabled by the expectation propagation algorithm. A novel parallel measurement processing expectation propagation unscented Kalman filter is developed. The primary advantage of the novel algorithm is in the ability to achieve computational improvements with negligible loses in filter accuracy. An example of robot localization with a high resolution laser rangefinder sensor is presented. A 47.53% decrease in computational time was exhibited for a scenario with a processing platform consisting of 4 processors, with a negligible loss in accuracy.

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Mobile robot localization via EKF and UKF: A comparison based on real data

Cited by 59 publications

References 18 publications

An Opportunistic Linear–Convex Algorithm for Localization in Mobile Robot Networks

An Opportunistic Linear–Convex Algorithm for Localization in Mobile Robot Networks

Research on EKF-Based Localization Method of Tracked Mobile Robot

A novel measurement processing approach to the parallel expectation propagation unscented Kalman filter

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