InCloud: Incremental Learning for Point Cloud Place Recognition

TL;DR

This paper introduces InCloud, an incremental learning method for point cloud place recognition that maintains performance across diverse environments by preventing catastrophic forgetting, validated on multiple large-scale datasets.

Contribution

InCloud is the first effective incremental learning approach for point cloud place recognition, preserving network structure during continual learning.

Findings

Broad performance improvements across datasets

Effective mitigation of catastrophic forgetting

Applicable to various network architectures

Abstract

Place recognition is a fundamental component of robotics, and has seen tremendous improvements through the use of deep learning models in recent years. Networks can experience significant drops in performance when deployed in unseen or highly dynamic environments, and require additional training on the collected data. However naively fine-tuning on new training distributions can cause severe degradation of performance on previously visited domains, a phenomenon known as catastrophic forgetting. In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space. We introduce several challenging new benchmarks on four popular and large-scale LiDAR datasets (Oxford, MulRan, In-house and KITTI) showing broad improvements in point cloud place recognition performance over a variety of network architectures. To the best of our knowledge, this work is the first to effectively apply incremental learning for point cloud place recognition. Data pre-processing, training and evaluation code for this paper can be found at https://github.com/csiro-robotics/InCloud.