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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

186
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
186

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Condition-Invariant Robot Localization Using Global Sequence Alignment of Deep Features.

Junghyun Oh1, Changwan Han1, Seunghwan Lee2

  • 1Department of Robotics, Kwangwoon University, Seoul 01897, Korea.

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Summary
This summary is machine-generated.

This study presents a robust visual localization system for robots navigating changing environments. It accurately identifies locations over long periods, outperforming existing methods in challenging conditions.

Keywords:
deep learninglocalizationplace recognitionroboticssequence alignment

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Robot localization is crucial for autonomous navigation and mapping.
  • Identifying locations in dynamic, long-term environments presents significant challenges due to appearance changes.

Purpose of the Study:

  • To develop a robust visual localization system capable of handling severe appearance changes in the environment.
  • To improve the accuracy and reliability of robot localization for large-scale and long-term operations.

Main Methods:

  • Utilized a deep variational autoencoder for robust feature extraction and image similarity calculation.
  • Implemented a global sequence alignment technique with a rectangle chaining algorithm to determine the robot's trajectory.
  • Incorporated robot motion constraints into the sequence alignment process.

Main Results:

  • The proposed system demonstrated superior performance in long-term visual localization under severe appearance variations.
  • The method effectively recovered from false matches and partial alignment failures.
  • Experimental results confirmed the system's accuracy and robustness compared to existing algorithms.

Conclusions:

  • The developed visual localization system offers a robust solution for robots operating in changing environments.
  • The combination of deep feature extraction and sequence alignment enhances localization accuracy and reliability.
  • This approach is particularly beneficial for long-term robot operations requiring consistent place recognition.