A Lightweight sequence-based Unsupervised Loop Closure Detection

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.