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Object Detection Applied to Indoor Environments for Mobile Robot Navigation.

Alejandra Carolina Hernández1, Clara Gómez2, Jonathan Crespo3

  • 1Department of Systems Engineering and Automation, Carlos III University of Madrid, Madrid 28911, Spain. alejandracarolina.hernandez@alumnos.uc3m.es.

Sensors (Basel, Switzerland)
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Summary

This study presents a vision system for mobile robots to detect and locate objects in indoor environments using Support Vector Machine (SVM) classification with RGB and depth images. Geometric descriptors and bag-of-words methods were compared for feature extraction, showing system usefulness.

Keywords:
Support Vector Machinemobile robotsobject classificationobject detectionrobot navigationshapes descriptors

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Human navigation heavily relies on sight for environmental understanding.
  • Autonomous mobile systems require robust object detection and recognition in indoor settings.
  • Developing effective vision systems for real-world mobile robot applications is a significant challenge.

Purpose of the Study:

  • To develop a vision system for object detection and location on a mobile robot operating in typical indoor environments.
  • To evaluate the performance of Support Vector Machine (SVM) classification using RGB and depth images.
  • To compare different feature extraction techniques for object recognition.

Main Methods:

  • Utilized a Support Vector Machine (SVM) classifier.
  • Employed both RGB and depth images as input data.
  • Applied various segmentation techniques tailored to different object types.
  • Explored two distinct feature extraction methods: geometric shape descriptors and bag of words.

Main Results:

  • The developed vision system demonstrated effectiveness in detecting and locating objects within indoor environments.
  • Experimental results validated the system's utility for mobile robot navigation.
  • A comparative analysis identified the superior performance of one feature extraction method over the other.

Conclusions:

  • The proposed vision system is a valuable tool for object detection and location in unaltered indoor environments for mobile robots.
  • The choice of feature extraction method significantly impacts the system's performance.
  • The integration of RGB and depth data enhances object recognition capabilities for autonomous systems.