Cost-effective Indoor Localization for Autonomous Robots using Kinect and WiFi Sensors
| Autor: | Josué Silva de Morais, Keiji Yamanaka, Raul Cesar Alves |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Real-time computing Process (computing) Initialization 0102 computer and information sciences 02 engineering and technology Kidnapped robot problem 01 natural sciences lcsh:QA75.5-76.95 010201 computation theory & mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Point (geometry) lcsh:Electronic computers. Computer science Divergence (statistics) Software |
| Zdroj: | Inteligencia Artificial, Vol 23, Iss 65 (2020) |
| ISSN: | 1988-3064 1137-3601 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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