Accurate localization of indoor high similarity scenes using visual slam combined with loop closure detection algorithm

Accurate localization is a critical technology for the application of intelligent robots and automation systems in complex indoor environments. Traditional visual SLAM (Simultaneous Localization and Mapping) techniques often face challenges with localization accuracy in high similarity scenes. To address this issue, this paper proposes an improved visual SLAM loop closure detection algorithm that integrates deep learning techniques. Using the TUM f3 loh, Lip6 Indoor, and Bicocca Indoor datasets as experimental bases, a detailed comparison of the proposed algorithm against other methods was conducted across various evaluation metrics. The experimental results show that the proposed loop closure detection algorithm significantly outperforms traditional methods in terms of localization accuracy in high similarity scenes. Specifically, the detection accuracy rates for the TUM f3 loh, Lip6 Indoor, and Bicocca Indoor datasets were 66.67%, 72.72%, and 80.00%, respectively, representing an approximate 18% improvement over the average accuracy of ORB-SLAM2. Additionally, the proposed method demonstrated excellent performance in trajectory error, with a root mean square error (RMSE) of just 0.0816m on the Bicocca Indoor dataset, significantly lower than the 0.1341m RMSE of ORB-SLAM2. Furthermore, improvements in feature extraction and matching mechanisms greatly reduced the occurrence of mismatches, enhancing the system's adaptability for more accurate localization and navigation in complex indoor environments. The proposed method effectively enhances localization accuracy and system practicality in visually similar indoor environments, offering a new direction for the development of visual SLAM technology and holding significant application potential in intelligent robots and indoor navigation systems.