KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition

Place recognition plays an important role in robot localization and SLAM. Being able to retrieve the current position in a given map allows, for instance, localizing without relying on GPS reception. In this letter, we address the problem of point cloud-based place recognition, we especially focus on reducing the often significant training time needed by learning-based approaches. We propose a novel neural network architecture that first extracts local features using a pre-trained encoder network plus a stem architecture. The local features are aggregated to a global descriptor, which allows us to compute the similarity between locations. In line with with several existing approaches, we target the generation of descriptors, which are similar for spatially near locations and dissimilar to other places. By exploiting the recent success of feature banks, we are able to bypass the computation of the negative examples, which enables faster training, bigger batch sizes, or the use of more sophisticated networks. As a key result, able to speed up the training process by a factor of 17 against the most common training procedure while increasing also the performance.

Automated summary

This letter focuses on point cloud-based place recognition and proposes a novel neural network architecture to reduce the training time. It extracts local features and computes the similarity between locations based on a global descriptor. By utilizing feature banks, faster training and improved performance are achieved.