Paper Info
Title
A Lightweight sequence-based Unsupervised Loop Closure Detection
Abstract
Stable, effective and lightweight loop closure detection is an always pursued goal in real-time SLAM systems, that can be ported on embedded processors and deployed on autonomous robotics. Deep learning methods have extended the expressive ability and adaptability of the descriptor, and sequence-based methods can greatly improve the matching accuracy. However, the increased computation complexity and storage bandwidth requirements of matching calculations for high-dimensional descriptor make it infeasible for real-time deployment, especially for robots that navigate in relatively big maps. To address this challenge, we propose a lightweight sequence-based unsupervised loop closure detection scheme. To be specific, Principal Component Analysis (PCA) is applied to squeeze the descriptor dimensions while maintaining sufficient expressive ability. Additionally, with the consideration of the image sequence and combining linear query with fast approximate nearest neighbor search to further reduce the execution time and improve the efficiency of sequence matching. We implement our method on CALC, a state-of-the-art unsupervised solution, and conduct experiments on NVIDIA TX2, results demonstrate that the accuracy has been improved by 5%, while the execution speed is 2x faster.
Year
DOI
Venue
2021
10.1109/IJCNN52387.2021.9534180
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Keywords
DocType
ISSN
Simultaneous Localization and Mapping, Loop Closure Detection, PCA, Images Sequence, Denoising Autoencoder
Conference
2161-4393
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Xiong Fan164.65
Yan Ding200.34
Mingrui Yu300.34
Wenzhe Zhao401.69
Nanning Zheng56521.36
 Pengju Ren613817.76