A Survey on Reinforcement Learning Applications in SLAM
TL;DR
This study explores the application of RL in the context of SLAM by enabling the agent to iteratively interact with and receive feedback from its environment, which facilitates the acquisition of navigation and mapping skills, thereby enhancing the robot's decision-making capabilities.
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 (RL), present viable solutions to address the challenges in SLAM. This study specifically explores the application of RL in the context of SLAM. By enabling the agent (the robot) to iteratively interact with and receive feedback from its environment, RL 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 provides an overview of RL's utilization in SLAM, reveal significant advancements in the field. The investigation also highlights the evolution and innovative integration of these techniques.
