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A Generic Image Processing Pipeline for Enhancing Accuracy and Robustness of Visual Odometry.

Mohamed Sabry1, Mostafa Osman2, Ahmed Hussein3

  • 1Autonomous Mobility and Perception Lab (AMPL), Universidad Carlos III de Madrid (UC3M), 28911 Leganes, Spain.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel image processing pipeline to improve visual odometry (VO) accuracy. The pipeline enhances feature matching robustness against lighting changes and outliers, boosting performance without increasing computational cost.

Keywords:
computer visionimage processing pipelinerobot operating system (ROS)visual odometry

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Area of Science:

  • Robotics
  • Computer Vision
  • Image Processing

Background:

  • Visual Odometry (VO) accuracy is challenged by environmental factors like lighting variations and feature matching outliers.
  • Feature-based VO algorithms are susceptible to performance degradation due to these factors.

Purpose of the Study:

  • To propose a generic and modular image processing pipeline to enhance the accuracy and robustness of feature-based VO algorithms.
  • To address lighting conditions, feature distribution, and outlier rejection in VO.

Main Methods:

  • Implemented Contrast Limited Adaptive Histogram Equalization (CLAHE) for lighting normalization.
  • Utilized the Suppression via Square Covering (SSC) algorithm for improved feature distribution.
  • Introduced the Angle-based Outlier Rejection (AOR) algorithm for robust outlier removal.

Main Results:

  • The pipeline demonstrated significant improvements in VO accuracy and robustness across various datasets (KITTI, TUM) and robot platforms.
  • Validated effectiveness for monocular, RGB-D, and stereo VO configurations.
  • Achieved performance gains without compromising computational efficiency.

Conclusions:

  • The proposed pipeline effectively enhances feature-based VO performance by mitigating common issues.
  • It offers a versatile solution applicable to diverse VO systems and environments.
  • The method provides a substantial accuracy and robustness improvement over standard VO approaches.