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.
翻译:地点的确认是机器人的基本组成部分,近年来通过使用深层学习模式取得了巨大的改进。网络在部署于不可见或高度动态环境中时,其性能会显著下降,并需要就所收集的数据进行更多的培训。然而,对新培训分布进行天真的微调,可能会导致以前访问过的域域的性能严重退化,这是一种被称为灾难性的遗忘现象。在本文件中,我们讨论了点云点识别的递增学习问题,并采用了基于结构的蒸馏方法InClod,这种方法维护了网络嵌入空间的较高级结构。我们对四个广受欢迎的大型LiDAR数据集(Oxford、MulRan、Inter-house和KITTI)提出了几项具有挑战性的新基准,显示在一些网络结构的点云点识别性表现得到了广泛的改进。据我们所知,这项工作首先有效地应用了点云点识别的递增学习。