Place recognition is an essential and challenging task in loop closing and global localization for robotics and autonomous driving applications. Benefiting from the recent advances in deep learning techniques, the performance of LiDAR place recognition (LPR) has been greatly improved. However, current deep learning-based methods suffer from two major problems: poor generalization ability and catastrophic forgetting. In this paper, we propose a continual contrastive learning method, named CCL, to tackle the catastrophic forgetting problem and generally improve the robustness of LPR approaches. Our CCL constructs a contrastive feature pool and utilizes contrastive loss to train more transferable representations of places. When transferred into new environments, our CCL continuously reviews the contrastive memory bank and applies a distribution-based knowledge distillation to maintain the retrieval ability of the past data while continually learning to recognize new places from the new data. We thoroughly evaluate our approach on Oxford, MulRan, and PNV datasets using three different LPR methods. The experimental results show that our CCL consistently improves the performance of different methods in different environments outperforming the state-of-the-art continual learning method. The implementation of our method has been released at https://github.com/cloudcjf/CCL.

翻译：位置识别是机器人及自动驾驶应用中闭环检测与全局定位的一项关键且具有挑战性的任务。得益于近年来深度学习技术的进步，激光雷达位置识别（LPR）的性能得到了大幅提升。然而，现有基于深度学习的方法存在两大主要问题：泛化能力差和灾难性遗忘。本文提出一种名为CCL的持续对比学习方法，旨在解决灾难性遗忘问题，并全面提升LPR方法的鲁棒性。我们的CCL构建了一个对比特征池，并利用对比损失来训练更具可迁移性的位置表征。在迁移到新环境时，CCL持续回顾对比记忆库，并采用基于分布的知识蒸馏方法，在持续学习识别新数据中新位置的同时，维持对旧数据的检索能力。我们在Oxford、MulRan和PNV数据集上，使用三种不同的LPR方法对本文方法进行了全面评估。实验结果表明，CCL能够在不同环境中持续提升不同方法的性能，并优于当前最先进的持续学习方法。本文方法的实现代码已发布于https://github.com/cloudcjf/CCL。