Robust mobile robot self-localization by soft sensor paradigm

Maniscalco, Umberto; Infantino, Ignazio; Manfrè, Adriano

doi:10.1109/iris.2017.8250092

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…In our work, features are converted to image file which is used by algorithm to confirm location of robot in the environment by matching it with generated sample of map. Although, rapid localization systems or probabilistic approach based on Bayes Filter (Chia et al, 2020;Maniscalco et al, 2017) are accurate but computationally expensive, where conversion of the entire field into the geometrical coordinates demands more computational power for running the algorithm. By using defined tracks, our proposed algorithm involves less computation procedures with satisfactory results.…”

Section: Resultsmentioning

confidence: 99%

Self-Localization of Mobile Robot Using Map Matching Algorithm

Shaikh¹,

Faraz²,

Jafri³

et al. 2021

JEAS

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Section: Resultsmentioning

confidence: 99%

Self-Localization of Mobile Robot Using Map Matching Algorithm

Shaikh¹,

Faraz²,

Jafri³

et al. 2021

JEAS

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An extended analysis on tuning the parameters of Adaptive Monte Carlo Localization ROS package in an automated guided vehicle

Reis

Silva

Morandin

et al. 2021

Int J Adv Manuf Technol

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With a growth tendency, the employment of the Adaptive Monte Carlo Localization (AMCL) Robot Operational System (ROS) package does not reflect a more indepth discussion on its parameters tuning process. The authors usually do not describe it. This work aims to extend the analysis of the package's parameters distinct influence in an Automated Guided Vehicle (AGV) indoor localization. The experiments test parameters of the filter, the laser model, and the odometry model. Extending the previous analysis of seven parameters, the present research discusses another ten from the 22 configurable parameters of the package. An external visual vehicle pose tracking is used to compare the pose estimation from the localization package. Although the article does not propose the best parameter tuning, its results discuss how each tested parameter affects the localization. The paper's contribution is discussing the parameters variation impact on the AGV localization. It may help new researchers in the AMCL ROS package parameter tuning process. The results show minor changes in the default param-

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