A Survey of Robotic Monocular Pose Estimation

Area of Science:

• Robotics
• Computer Vision
• Artificial Intelligence

Background:

• Robotic monocular pose estimation is fundamental to neural-driven methods.
• It encompasses localization in monocular SLAM and object pose estimation (OPE).
• Current research integrates advanced neural techniques.

Purpose of the Study:

• To review and synthesize neural methods for robotic monocular pose estimation.
• To highlight the role of depth prediction, semantics, neural implicit representations, and LLMs.
• To discuss future research and applications in real-world robotics.

Main Methods:

• Integration of depth prediction neural networks.
• Utilization of semantic information for pose refinement.
• Application of neural implicit representations.
• Leveraging large language models (LLMs) for enhanced estimation.

Main Results:

• Neural methods significantly enhance monocular pose estimation accuracy.
• The mapping thread in SLAM benefits from robust pose estimation.
• Complete robotic monocular pose estimation is a viable module for practical applications.

Conclusions:

• Robotic monocular pose estimation is a critical component for advanced robotic systems.
• Future work should focus on further integrating AI and deep learning for more sophisticated capabilities.
• Potential applications span various fields requiring precise robotic perception and navigation.