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3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey.

Sumaira Manzoor1, Sung-Hyeon Joo1, Eun-Jin Kim1

  • 1Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea.

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Summary
This summary is machine-generated.

This study explores 3D visual recognition for autonomous robots, focusing on deep learning methods for object and place recognition. It guides future research in vision-based robotics applications.

Keywords:
3D detection dataset3D visual recognitionLiDARautonomous vehiclescameradeep learningobject detectionplace recognitionrobotic systemssensor fusionsensors

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • 3D visual recognition is crucial for autonomous robots in real-world applications like object tracking and human-robot interaction.
  • Social robots require robust environmental understanding for effective task assistance in professional and domestic settings.
  • Recent advancements in deep learning have significantly impacted 3D recognition capabilities.

Purpose of the Study:

  • To analyze data representation methods for 3D object and place recognition using deep learning.
  • To highlight the value of social robots and their reliance on advanced recognition.
  • To identify research gaps and provide a comprehensive overview of the field.

Main Methods:

  • Analysis of data representation methods based on various sensor modalities.
  • Application of deep learning models for 3D object and place recognition.
  • Review and comparison of existing 3D recognition datasets and performance metrics.

Main Results:

  • Identified key deep learning approaches for 3D visual recognition in robotics.
  • Summarized current 3D recognition datasets and their performance benchmarks.
  • Delineated critical research gaps and future research directions.

Conclusions:

  • Recent developments in deep learning-based 3D visual recognition are foundational for advancing robotics.
  • This survey serves as a guide for researchers and developers in vision-based robotics.
  • Further research is needed to address identified gaps and enhance robot perception capabilities.