Fusion Scene Context: Robust and Efficient LiDAR Place Recognition Across Season
Fengkui Cao, Yanpeng Jia, Ting Wang, Hesheng Wang, Xieyuanli Chen
Abstract
Place recognition is an important component for autonomous robot navigation. Many existing LiDAR-based place recognition methods encode the structural information of 3D LiDAR data into 2D image representations. However, most of these intermediates only exploit the projection in a single view, ignoring a great amount of useful information. In this paper, a compact fusion-view image representation of LiDAR point cloud is proposed to extract important structural information from different views. Our proposed method generates such fusion-view images using the corresponding geometric information among points highlighting the edges of objects. It then extracts texture features encoding the shapes and layouts of scene elements into global descriptors, where regional features are designed to adapt local discrepancies caused by seasonal changes. Extensive experiments on the Oxford RobotCar, NCLT, UTBM datasets and our cross-season dataset validate the proposed method and demonstrate its superior generalization performance under different LiDAR sensors and season shifts. Moreover, our proposed method can operate online with a single CPU, making it suitable for resource-limited real robot platforms.
BibTeX
@inproceedings{iros2025_fusionsceneconte,
title = {Fusion Scene Context: Robust and Efficient LiDAR Place Recognition Across Season},
author = {Fengkui Cao and Yanpeng Jia and Ting Wang and Hesheng Wang and Xieyuanli Chen},
booktitle = {IROS 2025},
year = {2025}
}