An improved loop closure detection algorithm based on ORB-SLAM2

This work proposes algorithm based on MobileNetV3 to address the challenges of decreasing localization accuracy and increasing cumulative errors in traditional visual simultaneous localization and mapping (SLAM) loop closure detection algorithms in complex environments such as illumination variations, dynamic scenes, and viewpoint changes. A pretrained MobileNetV3 model is utilized to extract robust image features, followed by principal component analysis (PCA) and whitening to reduce the dimensionality of feature vectors and improve computational efficiency. Cosine similarity is employed to compute a similarity matrix of image features, and loop closures are identified based on predefined thresholds. Experimental results demonstrated that our MobileNetV3-based algorithm outperformed several comparative methods. Compared with the Bag-of-Visual-Words (BOVW) based approach, when using the New College and City Centre datasets our method achieved improvements in detection accuracy of 18.5 %and 19.3%, and enhanced detection speed of 30.6% and 34.4%, respectively. These results meet the accuracy and real-time performance requirements of visual SLAM systems. Furthermore, the algorithm was integrated into ORB-SLAM2 by replacing its original BOVW loop closure module. Evaluations using the EuRoC dataset showed that the enhanced ORB-SLAM2 achieved a 23.8% improvement in localization accuracy, with the estimated trajectories significantly closer to the Ground Truth. These results validate the feasibility and effectiveness of our algorithm for use within SLAM systems.