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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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

Panagiotis Kasnesis^1,2, Christos Chatzigeorgiou³, Vasileios Doulgerakis³

¹University of West Attica, Department of Electrical and Electronic Engineering, Egaleo, 12241, Greece. pkasnesis@uniwa.gr.

Scientific Data

|August 3, 2024

Summary

This summary is machine-generated.

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.