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Global Localization of Unmanned Ground Vehicles Using Swarm Intelligence and Evolutionary Algorithms

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Abstract

Mobile robot localization is a complex task, specially in unstructured indoor environments, due to noise and wrong scan-to-map association. The localization procedure becomes critical when the vehicle has low confidence about its last pose estimate, situation that requires a global localization procedure. An intuitive approach to solve the Global Localization Problem (GLP) is to distribute several pose hypotheses all over the map and select the most likely one according to an optimization heuristic such as Monte Carlo, Swarm Intelligence or Evolutionary Algorithm. However, hardware limitations and environment characteristics may affect the localization efficacy. Furthermore, we found relatively few studies exploring the effectiveness and the computing cost of different localization methods under different scenarios e.g. offices, corridors and big warehouses. In this work, we analyze different global localization methods based on multi-hypothesis optimization metaheuristics. We use the scan-to-map matching error computed by a pose tracking algorithm, the Perfect Match (PM), as the metric to score the hypotheses. Our main contribution is to propose an enhanced localization system by integrating a multi-hypothesis global localization method with the PM. We also analyzed different optimization heuristics applied to the GLP under typical and special conditions. Using simulations and real-world experiments, we measured the success rate and computing cost using several population sizes. Results show that studied methods perform differently in distinct scenarios, but our proposals based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) showed an average success rate above 83%, while other methods did not achieved 80%. Furthermore, PM-based methods exhibit lower computing cost when compared to the traditional Adaptive Monte Carlo Localization (AMCL) after the 100th iteration. In summary, our study shows that the GA-based proposal, which performed slightly better than the PSO-based, represents the best candidate to integrate a robust localization system.

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Code and Data Availability

The source code developed by the authors for this study and the datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. The Perfect Match software, whose source code was used under license for the current study, is a third party property and so is not publicly available. The Intel Research Lab dataset is a public domain resource under license “Creative Commons CC0 1.0 Universal” and was obtained from the digital repository for MIT’s research DSpace@MIT, available at https://dspace.mit.edu/handle/1721.1/62287.

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Funding

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement N.777096 and from SEPIN/MCTI under the 4th Coordinated Call BR-EU in CIT.

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Authors and Affiliations

  1. Digital Systems Laboratory, Federal University of Bahia, Salvador, Bahia, Brazil

    João L. C. Carvalho, Paulo C. M. A. Farias & Eduardo Furtado Simas Filho

  2. Center for Science and Technology in Energy and Sustainability, Federal University of Recôncavo da Bahia, Feira de Santana, Bahia, Brazil

    João L. C. Carvalho

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Contributions

All authors (Carvalho, J.L.C., Farias, P.C.M.A. and Simas Filho, E.F.) contributed equally to the study conception, design of the experiments, data analysis and the paper writing/review. Carvalho, J.L.C. and Farias, P.C.M.A. were responsible for the experiments planning, setup and generation of the dataset. Carvalho, J.L.C. developed the source code of the proposed methods, the scripts for data analysis and conducted the execution of the experiments.

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Correspondence to João L. C. Carvalho.

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Carvalho, J.L.C., Farias, P.C.M.A. & Simas Filho, E.F. Global Localization of Unmanned Ground Vehicles Using Swarm Intelligence and Evolutionary Algorithms. J Intell Robot Syst 107, 45 (2023). https://doi.org/10.1007/s10846-023-01813-6

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