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Updated: Aug 13, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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A Robust Planar Marker-Based Visual SLAM.

Zhoubo Wang1, Zhenhai Zhang1, Wei Zhu1

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

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|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances Simultaneous Localization and Mapping (SLAM) using artificial markers for improved accuracy and robustness, especially in challenging conditions like rotational movements. The improved system offers stable camera pose estimation and metric scale recovery.

Keywords:
planar markerspose ambiguityvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional visual SLAM systems struggle with textureless environments, illumination changes, and motion blur, leading to inaccurate camera poses.
  • Monocular SLAM often fails to provide metric scale information, limiting its practical applications.
  • Artificial landmarks offer a more robust alternative to natural features for camera pose estimation.

Purpose of the Study:

  • To enhance the traditional SPM-SLAM system for improved robustness and accuracy in camera pose estimation.
  • To address limitations of existing SLAM methods in challenging environments and monocular scenarios.
  • To introduce a novel method for planar-marker pose estimation to resolve ambiguity.

Main Methods:

  • Improvements to initialization, keyframe insertion, and relocalization strategies within the SPM-SLAM framework.
  • Development of a novel technique for estimating marker poses from multiple images to overcome planar-marker pose ambiguity.
  • Integration of artificial markers into the environment for stable and accurate localization.

Main Results:

  • The enhanced SPM-SLAM system demonstrates superior accuracy across public benchmark sequences compared to state-of-the-art methods.
  • The system exhibits significantly improved robustness, particularly under rotational movements.
  • Accurate camera poses with metric scale are obtained, addressing a key limitation of monocular SLAM.

Conclusions:

  • The proposed enhancements significantly improve the performance of marker-based SLAM systems.
  • The novel planar-marker pose estimation method effectively resolves ambiguity issues.
  • The publicly available open-source code facilitates further research and development in visual SLAM.