A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM

Vinod Sharma,Xiaoyi Fang,Qiubo Zhong

doi:10.1155/2021/9978022

Abstract

Loop closure detection serves as the fulcrum of improving the accuracy and precision in simultaneous localization and mapping (SLAM). The majority of loop detection methods extract artificial features, which fall short of learning comprehensive data information, but unsupervised learning as a typical deep learning method excels in self-access learning and clustering to analyze the similarity without handling the data. Moreover, the unsupervised learning method does solve restrictions on image quality and singleness semantics in many traditional SLAM methods. Therefore, a loop closure detection strategy based on an unsupervised learning method is proposed in this paper. The main component adopts BigBiGAN to extract features and establish an original bag of words. Then, the complete bag of words is used to detect loop closing. Finally, a considerable validation check of the ORB descriptor is added to verify the result and output outcome of loop closure detection. The proposed algorithm and other compared algorithms are, respectively, applied on Autolabor Pro1 to execute the indoor visual SLAM. The experiment shows that the proposed algorithm increases the recall rate by 20% compared with ORB-SLAM2 and LSD-SLAM. And it also improves at least 40.0% accuracy than others and reduces 14% time loss of ORB-SLAM2. Therefore, the presented SLAM based on BigBiGAN does benefit much the visual SLAM in the indoor environment.

Highlights

Introduction e classic visualsimultaneous localization and mapping (SLAM) [1] system mainly includes four processes: front end, back end, loop detection [2,3,4], and mapping. e front end aims to (1) extract feature points from image sets, (2) calculate motion trajectory, and (3) establish initial mapping
Loop closure detection could cut down accumulative errors or drifted outcomes in the progression
It could result in (1) worse computation estimation and (2) the unserviceable constructed tracking map. ereby, loop closure detection plays a significant part in the visual SLAM framework to effectively reduce accumulative errors and feature redundancy as much as possible by detecting overlapping features

Summary

Qiubo Zhong and Xiaoyi Fang

Received 4 March 2021; Revised 19 June 2021; Accepted 12 July 2021; Published 21 July 2021. Journal of Electrical and Computer Engineering constructed a dictionary using the CNN (convolutional neural network) to extract image features, which was different from the traditional bag-of-words model, and achieved a more accurate effect in loop detection. Zhang [10] proposed an unsupervised deep structure of the generative adversarial network (GAN) to detect features instead of using artificial ones, which performed well in the outdoor environment. Li et al [11] proposed a two-image fusion method which used RGB-D images in feature extraction and convolutional neural networks in loop detection to get higher accuracy and real-time capability. E experiment shows that the presented algorithm performs well in avoiding the shortcomings of using artificial image features and improving the (1) detection accuracy, (2) detection efficiency, and (3) time-consuming issue through the generative adversarial model’s learning results

Materials and Methods

Distinguish between the generated data and original data

Results and Discussion

Length Y

Real data ORB algorithm LSD algorithm Our algorithm

Number of real loops