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Sensor Data Fusion for a Mobile Robot Using Neural Networks.

Andres J Barreto-Cubero1, Alfonso Gómez-Espinosa1, Jesús Arturo Escobedo Cabello1

  • 1Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. Epigmenio González 500, Fracc. San Pablo, Querétaro 76130, Mexico.

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Summary
This summary is machine-generated.

This study introduces an improved LiDAR system using sensor fusion for mobile robots. It accurately detects glass and other obstacles, enhancing navigation safety and efficiency in indoor environments.

Keywords:
artificial neural networkimproved LiDARmobile robotoccupancy grid mapsensor data fusion

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Mobile robots require accurate mapping for navigation.
  • Detecting diverse materials, like glass, necessitates multi-sensor approaches.
  • Traditional 2D LiDAR struggles with certain obstacles.

Purpose of the Study:

  • To develop a robust sensor fusion scheme for enhanced mobile robot perception.
  • To improve the detection of glass and other challenging obstacles.
  • To generate accurate 2D occupancy grid maps (OGM) for improved navigation.

Main Methods:

  • Implemented a tri-sensor setup: RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors.
  • Utilized an artificial neural network for data fusion.
  • Applied preprocessing: outlier filtering, 3D pointcloud projection, and distance data adjustment.

Main Results:

  • Achieved accurate distance-to-obstacle readings by integrating multi-sensor data.
  • Generated a 2D Occupancy Grid Map (OGM) incorporating all sensor information.
  • Demonstrated effective detection of glass and other obstacles with an RMSE of 3 cm.

Conclusions:

  • The proposed sensor fusion strategy significantly enhances obstacle detection capabilities for mobile robots.
  • The artificial neural network effectively fuses data from multiple sensors for improved mapping.
  • This approach offers a more reliable navigation solution, especially in complex indoor environments.