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Deep Learning for Visual Localization and Mapping: A Survey.

Changhao Chen, Bing Wang, Chris Xiaoxuan Lu

IEEE Transactions on Neural Networks and Learning Systems

|September 22, 2023

Summary

This summary is machine-generated.

Deep learning offers a data-driven approach to localization and mapping (SLAM), outperforming traditional methods. This survey explores its potential for mobile agents, guiding future research in robotics and computer vision.

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Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Traditional localization and mapping rely on hand-designed algorithms.
Deep learning presents a data-driven alternative for these tasks.
Advancements in data and computation fuel deep learning's progress.

Purpose of the Study:

To survey deep-learning-based localization and mapping methods.
To establish a taxonomy for these emerging techniques.
To assess deep learning's viability and application in SLAM.

Main Methods:

Comprehensive review of recent literature.
Categorization of methods including visual odometry, relocalization, and SLAM.
Analysis of data-driven approaches versus model-based ones.

Main Results:

Deep learning shows significant promise for accurate and robust self-motion tracking and environmental modeling.
A clear taxonomy is proposed for deep learning in localization and mapping.
Recent works from multiple AI fields are synthesized.

Conclusions:

Deep learning is a promising direction for localization and mapping.
This survey provides a guideline for applying deep learning to visual localization and mapping.
The field is rapidly evolving, integrating robotics, computer vision, and machine learning.