4 papers
Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM
Neng Wang, Huimin Lu, Zhiqiang Zheng +3
Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environmen…
BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model
Ziyue Wang, Chenghao Shi, Neng Wang +3
Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently,…
SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
Neng Wang, Ruibin Guo, Chenghao Shi +5
4D LiDAR semantic segmentation, also referred to as multi-scan semantic segmentation, plays a crucial role in enhancing the environmental understanding capabilities of autonomous v…
SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
Neng Wang, Xieyuanli Chen, Chenghao Shi +3
Loop closing is a crucial component in SLAM that helps eliminate accumulated errors through two main steps: loop detection and loop pose correction. The first step determines wheth…