Enhancing Cooperative Visual SLAM with a Self-Supervised Deep Learning Model for Efficient Keypoint-Based Inter-map Loop Closure Detection

This paper introduces a self-supervised deep learning keypoint model SiLK specifically designed for loop closure detection in Cooperative Visual SLAM (Simultaneous Localization and Mapping). Firstly, the paper proposes a self-supervised learning framework that improves the robustness and accuracy of loop closure detection. Secondly, it presents the implementation of this deep learning keypoint model which replaces traditional manual feature descriptors, showcasing substantial enhancements in detecting loop closures across diverse and dynamic environments. This advancement ensures greater adaptability to variations in lighting and scene changes. Third, the paper illustrates the benefits of leveraging self-supervised learning within a cooperative visual SLAM context, where multiple agents share and fuse their local observations. This collaborative effort leads to refined pose estimations and more accurate mapping outcomes, enhancing the overall system’s performance in complex settings. The effectiveness of the proposed model was rigorously tested in real-world scenarios, demonstrating its superiority in robustness and mapping precision compared to traditional methods in cooperative robotic systems, including those used for environmental monitoring and exploration tasks. The results highlight the potential of self-supervised deep learning models to revolutionize loop closure detection in visual SLAM, offering a promising avenue for future research in autonomous systems and cooperative robotics in challenging operational environments.