SG-LPR: Semantic-Guided LiDAR-Based Place Recognition

doi:10.3390/electronics13224532

	SG-LPR: Semantic-Guided LiDAR-Based Place Recognition
	Jiang, Weizhong 1; Xue, Hanzhang 1,2; Si, Shubin 1,3; Min, Chen 4; Xiao, Liang 1; Nie, Yiming 1; Dai, Bin 1
	2024-11-01
发表期刊	ELECTRONICS
卷号	13 期号:22 页码:21
摘要	Place recognition plays a crucial role in tasks such as loop closure detection and re-localization in robotic navigation. As a high-level representation within scenes, semantics enables models to effectively distinguish geometrically similar places, therefore enhancing their robustness to environmental changes. Unlike most existing semantic-based LiDAR place recognition (LPR) methods that adopt a multi-stage and relatively segregated data-processing and storage pipeline, we propose a novel end-to-end LPR model guided by semantic information-SG-LPR. This model introduces a semantic segmentation auxiliary task to guide the model in autonomously capturing high-level semantic information from the scene, implicitly integrating these features into the main LPR task, thus providing a unified framework of "segmentation-while-describing" and avoiding additional intermediate data-processing and storage steps. Moreover, the semantic segmentation auxiliary task operates only during model training, therefore not adding any time overhead during the testing phase. The model also combines the advantages of Swin Transformer and U-Net to address the shortcomings of current semantic-based LPR methods in capturing global contextual information and extracting fine-grained features. Extensive experiments conducted on multiple sequences from the KITTI and NCLT datasets validate the effectiveness, robustness, and generalization ability of our proposed method. Our approach achieves notable performance improvements over state-of-the-art methods.
关键词	LiDAR-based place recognition semantic-guided auxiliary task swin transformer U-Net
DOI	10.3390/electronics13224532
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Physics
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:001364355000001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41151
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xiao, Liang; Nie, Yiming
作者单位	1.Def Innovat Inst, Unmanned Syst Technol Res Ctr, Beijing 100071, Peoples R China 2.Natl Univ Def Technol, Test Ctr, Xian 710106, Peoples R China 3.Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Weizhong,Xue, Hanzhang,Si, Shubin,et al. SG-LPR: Semantic-Guided LiDAR-Based Place Recognition[J]. ELECTRONICS,2024,13(22):21.
APA	Jiang, Weizhong.,Xue, Hanzhang.,Si, Shubin.,Min, Chen.,Xiao, Liang.,...&Dai, Bin.(2024).SG-LPR: Semantic-Guided LiDAR-Based Place Recognition.ELECTRONICS,13(22),21.
MLA	Jiang, Weizhong,et al."SG-LPR: Semantic-Guided LiDAR-Based Place Recognition".ELECTRONICS 13.22(2024):21.