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RGB-D Object SLAM Using Quadrics for Indoor Environments.

Ziwei Liao1, Wei Wang1, Xianyu Qi1

  • 1Robotics Institute, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|September 12, 2020
PubMed
Summary

This study introduces an object-level semantic SLAM algorithm using RGB-D data for indoor robots. It enhances robot environmental understanding and instruction execution through improved localization and mapping accuracy.

Keywords:
RGB-Ddata associationindoor environmentsmobile robotsnonparametric pose graphobject SLAMquadricssemantic SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Conventional Simultaneous Localization and Mapping (SLAM) systems lack semantic understanding for object-level environmental mapping.
  • Indoor service robots require object-centric semantic maps to interpret and follow human commands effectively.

Purpose of the Study:

  • To propose an object-level semantic SLAM algorithm leveraging RGB-D data for enhanced indoor robot navigation and interaction.
  • To develop novel RGB-D camera-quadric observation models for accurate object representation and mapping.

Main Methods:

  • Utilized a quadric surface as an object model for compact representation of object pose and shape.
  • Introduced complete and partial RGB-D camera-quadric observation models, incorporating object detection and point cloud data.
  • Implemented a nonparametric pose graph for robust data association in the backend, applied to the quadric surface model.

Main Results:

  • The proposed RGB-D observation model improved accuracy and robustness compared to monocular models, reducing observation number and viewing angle change requirements.
  • Demonstrated significant improvements in localization accuracy and mapping quality on public and custom datasets.
  • Outperformed two state-of-the-art object SLAM algorithms in evaluations.

Conclusions:

  • The developed object-level semantic SLAM algorithm provides superior localization and mapping capabilities for indoor robots.
  • The novel RGB-D observation models and nonparametric pose graph integration enhance the performance and reliability of semantic SLAM systems.