Ranking-aware Continual Learning for LiDAR Place Recognition

TL;DR

This paper proposes a ranking-aware continual learning framework for LiDAR place recognition that mitigates catastrophic forgetting by using knowledge distillation and fusion, improving performance over existing methods.

Contribution

It introduces a novel ranking-aware knowledge distillation loss and a knowledge fusion module specifically designed for LiDAR place recognition in continual learning settings.

Findings

KDF surpasses state-of-the-art in mean Recall@1

KDF effectively reduces forgetting in continual learning

Applicable to various network architectures

Abstract

Place recognition plays a significant role in SLAM, robot navigation, and autonomous driving applications. Benefiting from deep learning, the performance of LiDAR place recognition (LPR) has been greatly improved. However, many existing learning-based LPR methods suffer from catastrophic forgetting, which severely harms the performance of LPR on previously trained places after training on a new environment. In this paper, we introduce a continual learning framework for LPR via Knowledge Distillation and Fusion (KDF) to alleviate forgetting. Inspired by the ranking process of place recognition retrieval, we present a ranking-aware knowledge distillation loss that encourages the network to preserve the high-level place recognition knowledge. We also introduce a knowledge fusion module to integrate the knowledge of old and new models for LiDAR place recognition. Our extensive experiments demonstrate that KDF can be applied to different networks to overcome catastrophic forgetting, surpassing the state-of-the-art methods in terms of mean Recall@1 and forgetting score.