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Real-time 2D-3D door detection and state classification on a low-power device.

João Gaspar Ramôa1,2, Vasco Lopes1,2, Luís A Alexandre1,2

  • 1NOVA LINCS, Costa da Caparica, Portugal.

SN Applied Sciences
|May 4, 2021
PubMed
Summary

We developed three novel methods for classifying door states (open, closed, semi-open) to enhance robot navigation. These algorithms run in real-time on low-power devices like the Jetson Nano.

Keywords:
2D–3D Door datasetDoor detectionDoor segmentationDoor state classificationJetson nanoReal-Time

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Robot navigation in indoor environments requires accurate perception of surroundings.
  • Distinguishing door states (open, closed, semi-open) is crucial for efficient path planning.
  • Existing methods often lack real-time performance or are limited to specific hardware.

Purpose of the Study:

  • To propose and evaluate three distinct methods for door state classification.
  • To enable real-time door state recognition on low-power computing platforms.
  • To develop versatile algorithms applicable beyond door detection.

Main Methods:

  • Utilized 3D object classification (PointNet) and real-time semantic segmentation (FastFCN, FC-HarDNet, SegNet, BiSeNet).
  • Employed object detection (DetectNet) and 2D classification networks (AlexNet, GoogleNet).
  • Created a comprehensive 3D and RGB door dataset using a Realsense D435 camera.

Main Results:

  • All proposed methods achieved real-time performance on a low-power Jetson Nano device.
  • The algorithms successfully differentiated between open, closed, and semi-open doors.
  • Accuracy and speed were analyzed for each method on the low-power computer.

Conclusions:

  • Real-time door classification on low-power devices is feasible.
  • The developed methods offer a flexible solution for robot navigation and other applications.
  • The freely available dataset supports further research in indoor environment perception.