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Introducing an indoor object classification dataset including sparse point clouds from mmWave radar.

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This study introduces the RadIOCD dataset, featuring millimeter-wave (mmWave) radar data for indoor object recognition. This dataset enables machine learning for applications where vision is limited, like search and rescue.

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Data

Background:

  • Object recognition is crucial for autonomous systems.
  • Vision-based methods are limited in occluded or smoky environments.
  • Millimeter-wave (mmWave) radar offers an alternative sensing modality.

Purpose of the Study:

  • Introduce the RadIOCD dataset for mmWave radar-based indoor object recognition.
  • Provide a comprehensive dataset for developing and testing machine learning models.
  • Enable object recognition in challenging environments where vision fails.

Main Methods:

  • Collected sparse 3D point cloud data using a commercial mmWave radar.
  • Recorded 10 volunteers interacting with 5 object types across 3 environments.
  • Captured data including Doppler velocity and intensity for each point cloud.

Main Results:

  • The RadIOCD dataset comprises 5,776 recordings, each approximately 8 seconds long.
  • The dataset was segmented into 76,821 samples suitable for machine learning.
  • Demonstrated the dataset's potential for training generalizable machine learning models.

Conclusions:

  • The RadIOCD dataset facilitates mmWave radar-based object recognition.
  • The dataset supports machine learning model development for robust object detection.
  • RadIOCD is valuable for applications requiring sensing in non-visual conditions.