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

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.