An adaptive network fusing light detection and ranging height-sliced bird’s-eye view and vision for place recognition
Publication Type
Journal Article
Publication Date
9-2024
Abstract
Place recognition, a fundamental component of robotic perception, aims to identify previously visited locations within an environment. In this study, we present a novel global descriptor that uses height-sliced Bird’s Eye View (BEV) from Light Detection and Ranging (LiDAR) and vision images, to facilitate high-recall place recognition in autonomous driving field. Our descriptor generation network, incorporates an adaptive weights generation branch to learn weights of visual and LiDAR features, enhancing its adaptability to different environments. The generated descriptor exhibits excellent yaw-invariance. The entire network is trained using a self-designed quadruplet loss, which discriminates inter-class boundaries and alleviates overfitting to one particular modality. We evaluate our approach on three benchmarks derived from two public datasets and achieve optimal performance on these evaluation sets. Our approach demonstrates excellent generalization ability and efficient runtime, which are indicative of its practical viability in real-world scenarios. For those interested in applying this Artificial Intelligence contribution to engineering, the implementation of our approach can be found at: https://github.com/Bryan-ZhengRui/LocFuse
Keywords
Multi-modal place recognition, Deep learning method, Sensor fusion Autonomous driving
Discipline
Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces
Research Areas
Information Systems and Management
Publication
Engineering Applications of Artificial Intelligence
Volume
137
First Page
1
Last Page
13
ISSN
0952-1976
Identifier
10.1016/j.engappai.2024.109230
Publisher
Elsevier
Citation
ZHENG, Rui; JIANG, Zuo; YE, Yibin; REN, Yang; ZENG, Hui; LI, Junwei; and ZHANG, Zhiyuan.
An adaptive network fusing light detection and ranging height-sliced bird’s-eye view and vision for place recognition. (2024). Engineering Applications of Artificial Intelligence. 137, 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/9310
Additional URL
https://doi.org/10.1016/j.engappai.2024.109230