A Survey on Reinforcement Learning Applications in SLAM

TL;DR

This survey reviews how reinforcement learning techniques are applied to improve autonomous navigation and mapping in SLAM, highlighting recent advancements and integration strategies in robotics.

Contribution

It provides a comprehensive overview of reinforcement learning applications in SLAM, emphasizing recent innovations and their impact on robotic navigation.

Findings

Reinforcement learning enhances robot navigation and decision-making in SLAM.

RL approaches increase resilience and reduce sensor dependence.

Significant progress in RL-based SLAM techniques has been achieved.

Abstract

The emergence of mobile robotics, particularly in the automotive industry, introduces a promising era of enriched user experiences and adept handling of complex navigation challenges. The realization of these advancements necessitates a focused technological effort and the successful execution of numerous intricate tasks, particularly in the critical domain of Simultaneous Localization and Mapping (SLAM). Various artificial intelligence (AI) methodologies, such as deep learning and reinforcement learning, present viable solutions to address the challenges in SLAM. This study specifically explores the application of reinforcement learning in the context of SLAM. By enabling the agent (the robot) to iteratively interact with and receive feedback from its environment, reinforcement learning facilitates the acquisition of navigation and mapping skills, thereby enhancing the robot's decision-making capabilities. This approach offers several advantages, including improved navigation proficiency, increased resilience, reduced dependence on sensor precision, and refinement of the decision-making process. The findings of this study, which provide an overview of reinforcement learning's utilization in SLAM, reveal significant advancements in the field. The investigation also highlights the evolution and innovative integration of these techniques.