Shaocong Wang — accepted AI-conference papers

2025

BEV-LSLAM: A Novel and Compact BEV LiDAR SLAM for Outdoor Environment

Fengkui Cao, Shaocong Wang, Xieyuanli Chen, Ting Wang, Lianqing Liu

RA-L 2025

LiDAR-based SLAM is an essential technology for autonomous robots, benefited from its high accuracy and scale invariance. Interestingly, researchers have been increasingly focusing on establishing simple, efficient, but effective LiDAR SLAM systems recently. In this paper, we propose a novel and com

Cited by 4 Source ↗Scholar ↗

2025

SGT-LLC: LiDAR Loop Closing Based on Semantic Graph With Triangular Spatial Topology

Shaocong Wang, Fengkui Cao, Ting Wang, Xieyuanli Chen, Shiliang Shao

RA-L 2025

Inspired by how humans perceive, remember, and understand the world, semantic graphs have become an efficient solution for place representation and location. However, many current graph-based LiDAR loop closing methods focus on extracting adjacency matrices or semantic histograms to describe the sce

Cited by 6 Source ↗Scholar ↗