Adaptive Gradient-Balanced Boundary Memory for Continual LiDAR Place Recognition

Abstract

Continual LiDAR place recognition requires a descriptor model to learn new environments while retaining retrieval accuracy in previously learned environments under limited access to historical data. Replaying historical retrieval relations and distilling descriptors impose different optimization demands that fixed loss coefficients cannot readily accommodate. We propose adaptive gradient-balanced boundary memory (AGBBM), a framework that operates on global descriptors without changing the backbone architecture. Boundary-aware triplet memory retains complete anchor–positive–negative relations and their historical distance statistics under an environment-balanced triplet budget. Reliability-guided decoupled distillation separates supervised replay, one-sided positive-distance preservation, and reliability-weighted historical-boundary preservation. A two-level optimization mechanism normalizes active historical losses using exponential moving averages and calibrates same-view descriptor distillation using the ratio of current-learning and distillation gradient norms at a shared descriptor parameter, with configurable attenuation under gradient conflict. We evaluate AGBBM in the stage-wise sequence Oxford → MulRan DCC → MulRan Riverside → In-house using PointNetVLAD, MinkLoc3D, LoGG3D-Net with max pooling, and CASSPR. The final mean Recall@1 values are 61.42%, 79.30%, 66.12%, and 82.07%, respectively, with signed average forgetting of 1.83, −0.17, 1.03, and 0.52 percentage points. The results support historical retrieval retention across the evaluated backbones and four-stage sequence. The evaluation addresses sequential descriptor learning rather than online obstacle detection or explicit map-change identification.