Shufflenet-le: an efficient shuffle attention-convolutional model for visual place recognition

In the field of robotics, visual place recognition using 3D point cloud data has become a pivotal area of research, playing a vital role in navigation and mapping systems. Among the various techniques, the scan-context descriptor imagery has emerged as an effective method for 3D point cloud-based localization. This paper presents a novel approach that combines the scan-context descriptor with the Shuffle Attention (SA) mechanism, creating a hybrid module aimed at improving the accuracy and efficiency of visual place recognition systems. Through extensive experiments on the NCLT dataset, our proposed method outperforms traditional approaches, showcasing its superiority in real-world place recognition tasks.