LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning

• URL: http://arxiv.org/abs/2507.10034v1
• Date: Mon, 14 Jul 2025 08:13:33 GMT
• Title: LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
• Authors: Xianghong Zou, Jianping Li, Zhe Chen, Zhen Cao, Zhen Dong, Qiegen Liu, Bisheng Yang,
• Abstract summary: Point cloud place recognition (PCPR) plays a crucial role in photogrammetry and robotics applications.<n>Existing PCPR models often suffer from catastrophic forgetting, leading to significant performance degradation.<n>We propose LifelongPR, a novel continual learning framework for PCPR, which effectively extracts and fuses knowledge from sequential point cloud data.
• Score: 15.464706470200337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud place recognition (PCPR) plays a crucial role in photogrammetry and robotics applications such as autonomous driving, intelligent transportation, and augmented reality. In real-world large-scale deployments of a positioning system, PCPR models must continuously acquire, update, and accumulate knowledge to adapt to diverse and dynamic environments, i.e., the ability known as continual learning (CL). However, existing PCPR models often suffer from catastrophic forgetting, leading to significant performance degradation in previously learned scenes when adapting to new environments or sensor types. This results in poor model scalability, increased maintenance costs, and system deployment difficulties, undermining the practicality of PCPR. To address these issues, we propose LifelongPR, a novel continual learning framework for PCPR, which effectively extracts and fuses knowledge from sequential point cloud data. First, to alleviate the knowledge loss, we propose a replay sample selection method that dynamically allocates sample sizes according to each dataset's information quantity and selects spatially diverse samples for maximal representativeness. Second, to handle domain shifts, we design a prompt learning-based CL framework with a lightweight prompt module and a two-stage training strategy, enabling domain-specific feature adaptation while minimizing forgetting. Comprehensive experiments on large-scale public and self-collected datasets are conducted to validate the effectiveness of the proposed method. Compared with state-of-the-art (SOTA) methods, our method achieves 6.50% improvement in mIR@1, 7.96% improvement in mR@1, and an 8.95% reduction in F. The code and pre-trained models are publicly available at https://github.com/zouxianghong/LifelongPR.

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