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Related Experiment Video

Updated: Jan 3, 2026

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Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation.

Wenlei Liu1, Sentang Wu1, Zhongbo Wu2

  • 1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|November 27, 2019
PubMed
Summary

This paper introduces an incremental pose map optimization for monocular vision simultaneous localization and mapping (SLAM) to solve scale drift and cumulative errors. The method enhances depth estimation robustness and camera pose accuracy for reliable SLAM.

Keywords:
bag-of-wordshistogram equalizationincremental pose mapmonocular vision SLAMprobability graphsimilarity transformationsparse direct method

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Monocular vision Simultaneous Localization and Mapping (SLAM) systems often suffer from scale drift and cumulative errors.
  • Accurate depth estimation is crucial for robust SLAM performance, but it is challenging with monocular input.

Purpose of the Study:

  • To propose an incremental pose map optimization method for monocular vision SLAM.
  • To address the scale drift problem and eliminate cumulative errors in SLAM.
  • To improve the robustness and accuracy of depth estimation.

Main Methods:

  • A hybrid front-end combining sparse direct and feature point methods with histogram equalization.
  • Mixed inverse depth estimation using a probabilistic graph model.
  • A bag-of-words model with mean-initialized K-means for loop closure detection.
  • Incremental pose map optimization based on similarity transformation for back-end processing.

Main Results:

  • The proposed method effectively solves the scale drift problem in monocular vision SLAM.
  • Cumulative errors are eliminated through global optimization upon loop closure detection.
  • Depth estimation uncertainty is reduced, enhancing overall system robustness.
  • Experimental validation on TUM and KITTI datasets demonstrates the method's effectiveness.

Conclusions:

  • The developed incremental pose map optimization offers a robust solution for monocular vision SLAM.
  • The integration of advanced depth estimation and loop closure techniques significantly improves system performance.
  • This approach provides a reliable method for accurate camera pose and depth estimation in dynamic environments.