TLCD: A Transformer based Loop Closure Detection for Robotic Visual SLAM

Chenghao Li; Hongwei Ren; Minjie Bi; Chenchen Ding; Wenjie Li; Rumin Zhang; Xiaoguang Liu; Hao Yu

10.1109/ICARM54641.2022.9959319

2022-07-11

978-1-6654-8307-0

2022 International Conference on Advanced Robotics and Mechatronics (ICARM)

9-11 July 2022

Guilin, China

Loop closure detection (LCD) can effectively correct errors in visual odometry. It is thereby a critical part in robotic visual simultaneous localization and mapping (SLAM) system, which is widely used in modern robotic systems such as sweeping robots and drones. In this paper, we propose a transformer-based loop closure detection algorithm (TLCD), which employs a distillation transformer as backbone to extract global features, and is combined with a sequence matching as back-end processing of principal component analysis (PCA) algorithm. TLCD can accurately provide Precision-Recall curve based on several public datasets including CityCentre and New-College datasets. Results show that TLCD’s average accuracy is up to 16.91% higher than the traditional LCD method. It is also about 3.18% higher accuracy than the state-of-the-art convolutional neural network (CNN) based LCD method.

其他

Microelectronics College of Engineering Southern University of Science and Technology, Nanshan District, Shenzhen, Guangdong, China

Chenghao Li,Hongwei Ren,Minjie Bi,et al. TLCD: A Transformer based Loop Closure Detection for Robotic Visual SLAM[C],2022.