Place Recognition
Place recognition, a crucial component of autonomous systems, aims to identify previously visited locations using sensor data. Current research emphasizes robust and efficient methods across diverse sensor modalities (cameras, LiDAR, radar), focusing on model architectures like transformers, convolutional neural networks, and novel feature aggregation techniques to handle variations in viewpoint, lighting, and environmental changes. These advancements are vital for improving the reliability and scalability of robotics, autonomous driving, and mapping applications, particularly in challenging, GPS-denied environments.
Papers
BPT: Binary Point Cloud Transformer for Place Recognition
Zhixing Hou, Yuzhang Shang, Tian Gao, Yan Yan
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections
Shuhang Zheng, Yixuan Li, Zhu Yu, Beinan Yu, Si-Yuan Cao, Minhang Wang, Jintao Xu, Rui Ai, Weihao Gu, Lun Luo, Hui-Liang Shen
A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition
Maria Waheed, Sania Waheed, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
T. Barros, L. Garrote, P. Conde, M. J. Coombes, C. Liu, C. Premebida, U. J. Nunes
Region Prediction for Efficient Robot Localization on Large Maps
Matteo Scucchia, Davide Maltoni
CASSPR: Cross Attention Single Scan Place Recognition
Yan Xia, Mariia Gladkova, Rui Wang, Qianyun Li, Uwe Stilla, João F. Henriques, Daniel Cremers
FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events
Kuanxu Hou, Delei Kong, Junjie Jiang, Hao Zhuang, Xinjie Huang, Zheng Fang