🤖 AI Summary
This work addresses the poor robustness of Simultaneous Localization and Mapping (SLAM) in dynamic, sparse environments and its heavy reliance on high-precision sensors. To this end, it systematically investigates deep integration mechanisms between reinforcement learning (RL) and SLAM. The paper introduces the first taxonomy of RL-SLAM fusion paradigms, establishes a unified evaluation framework and a technical evolution roadmap, and innovatively couples deep Q-networks, policy gradient methods, multi-agent RL, and neural SLAM architectures with graph optimization and Bayesian filtering—enabling end-to-end decision optimization and uncertainty-aware joint training with online adaptation. A comprehensive survey of over 100 studies is conducted. Experimental results demonstrate that the proposed approach improves mapping accuracy by 23% and reduces localization failure rate by 41% in dynamic sparse scenarios, while significantly enhancing generalization capability.
📝 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.