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Summary

This review explores learning-based methods for Visual Simultaneous Localisation and Mapping (SLAM) in robots. It highlights how deep learning improves environment reconstruction and robot localization compared to traditional geometric approaches.

Keywords:

Simultaneous Localisation and Mapping Visual SLAM camera deep-learning SLAM mobile robots navigation semantic understanding sensors fusion vision

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Area of Science:

Robotics and Autonomous Systems
Computer Vision
Artificial Intelligence

Background:

Simultaneous Localisation and Mapping (SLAM) is crucial for mobile robots to map unknown environments and track their position.
Traditional geometric methods for Visual-SLAM are often unreliable in complex environments.
Recent advances in deep learning offer promising data-driven solutions for Visual-SLAM challenges.

Purpose of the Study:

To review and summarize recent advancements in Visual-SLAM utilizing learning-based techniques.
To provide an overview of traditional geometric approaches and current SLAM paradigms.
To discuss deep learning applications in sensory data collection, scene understanding, and semantic understanding for Visual-SLAM.

Main Methods:

Review of existing literature on geometric model-based and learning-based Visual-SLAM.
Analysis of deep learning techniques applied to sensory input processing and scene understanding.
Discussion of current deep learning paradigms for semantic understanding within Visual-SLAM.

Main Results:

Learning-based methods show significant potential to overcome limitations of traditional geometric approaches in Visual-SLAM.
Deep learning enhances sensory data interpretation and scene understanding for more robust robot localization and mapping.
Semantic understanding through deep learning is a key area of advancement in Visual-SLAM.

Conclusions:

Learning-based approaches represent a paradigm shift in Visual-SLAM, offering improved performance in challenging scenarios.
Further research into deep learning for Visual-SLAM is essential to address existing challenges and unlock future opportunities.
The integration of AI and computer vision is critical for the advancement of autonomous mobile robot navigation.