Cost-effective indoor localization for autonomous robots using Kinect and WiFi sensors

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      <subfield code="a">Alves, Raulcézar </subfield>
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      <subfield code="a">Cost-effective indoor localization for autonomous robots using Kinect and WiFi sensors</subfield>
      <subfield code="c">Raulcézar Alves, Josué Silva de Morais, and Keiji Yamanaka</subfield>
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      <subfield code="a">Indoor localization has been considered to be the most fundamental problem when it comes to providing a robot with autonomous capabilities. Although many algorithms and sensors have been proposed, none have proven to work perfectly under all situations. Also, in order to improve the localization quality, some approaches use expensive devices either mounted on the robots or attached to the environment that don't naturally belong to human environments. This paper presents a novel approach that combines the benefits of two localization techniques, WiFi and Kinect, into a single algorithm using low-cost sensors. It uses separate Particle Filters (PFs). The WiFi PF gives the global location of the robot using signals of Access Point devices from different parts of the environment while it bounds particles of the Kinect PF, which determines the robot's pose locally. Our algorithm also tackles the Initialization/Kidnapped Robot Problem by detecting divergence on WiFi signals, which starts a localization recovering process. Furthermore, new methods for WiFi mapping and localization are introduced.</subfield>
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      <subfield code="a">Automatización</subfield>
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      <subfield code="a">Silva de Morais, Josué </subfield>
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      <subfield code="a">Yamanaka, Keiji </subfield>
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      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d">IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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      <subfield code="g">01/06/2020 Volumen 23 Número 65 - junio 2020 , p. 33-55</subfield>
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