Self-Localization of Mobile Robot Using Map Matching Algorithm

Shaikh, Bushra; Faraz, Sadia Muniza; Jafri, Syed Riaz un Nabi; Ali, Syed M. Usman

doi:10.17582/journal.jeas/40.1.69.77

Cited by 1 publication

(1 citation statement)

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“…The self-localization of guide robots was the subject of the deep-learning approach suggested by Smith and Johnson in 2022. This strategy produced encouraging outcomes and concentrated on enhancing the robots' capacity to ascertain their own position in varied surroundings 2 . creating a pathplanning system suitable for indoor environments for visually impaired people 3 .…”

Section: Introductionmentioning

confidence: 99%

Self-Localization of Guide Robots Through Image Classification

Alam,

Mohamed,

Hossain

2024

Baghdad Sci.J

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The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accurate solution for indoor robot navigation. The more accurate solution of the guide robotic system opens a new window of the self-localization system and solves the more complex problem of indoor robot navigation. It makes a reliable interface between humans and robots. This study successfully demonstrated how a robot finds its initial position inside a room. A deep learning system, such as a convolutional neural network, trains the self-localization system as an image classification problem. The robot was placed inside the room to collect images using a panoramic camera. Two datasets were created from the room images based on the height above and below the chest. The above-mentioned method achieved a localization accuracy of 98.98%.

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Section: Introductionmentioning

confidence: 99%