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：摘要：地点识别是机器人及自动驾驶应用中闭环检测与全局定位中的一项关键且具有挑战性的任务。得益于深度学习技术的最新进展，激光雷达地点识别的性能已显著提升。然而，当前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。本文提出了一种名为CCL的持续对比学习方法，以解决灾难性遗忘问题并全面增强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地点表征。当迁移至新环境时，CCL持续回顾对比记忆库，并应用基于分布的知识蒸馏，在持续学习从新数据中识别新地点的同时，维持对历史数据的检索能力。我们使用三种不同的LPR方法在Oxford、MulRan和PNV数据集上对方法进行了全面评估。实验结果表明，CCL在不同环境中持续提升了不同方法的性能，超越了现有最优的持续学习方法。本方法的实现代码已发布于 https://github.com/cloudcjf/CCL。