Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: International Conference on Image, Video Processing and Artificial Intelligence (IVPAI)

DOI: 10.1117/12.3074973

Authors: Minying YE, Yu Liu

Institutions: University of Fukui, Xi'an University of Technology

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: Strong

OpenAlex ID: W4413819900