OCLPlace: Online Continual Learning on LiDAR Streams for Place Recognition

Abstract

LiDAR place recognition is a critical component of LiDAR-based localization pipelines, tasked with identifying previously visited places across diverse environments and temporal conditions. A growing body of deep learning–based approaches has recently tackled this problem. However, their performance often degrades when the models are deployed in unseen environments. Although offline fine-tuning can partly recover performance, it is prone to catastrophic forgetting of previously acquired knowledge and cannot respond quickly enough to rapidly changing data distributions. In this paper, we introduce OCLPlace, an online continual learning framework that learns directly from highly temporally correlated LiDAR streams and strikes a trade-off between rapid domain adaptation and resistance to catastrophic forgetting. To the best of our knowledge, OCLPlace is the first LiDAR place-recognition approach enhanced by online continual learning that can automatically adapt to new environments while mitigating catastrophic forgetting. Experimental results on six large-scale datasets, which cover both ground-view and aerial-view scenarios, demonstrate the effectiveness and robustness of our method. The source code will be publicly available at: https://github.com/npu-ius-lab/OCLPlace.