activity
20222024
most citedPALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation

19 citations · 21 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2024

MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System

Xiangcheng Hu, Jin Wu, Jianhao Jiao +4

Large-scale multi-session LiDAR mapping is essential for a wide range of applications, including surveying, autonomous driving, crowdsourced mapping, and multi-agent navigation. Ho…

cs.RO20241 cited

MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection

Xiangcheng Hu, Jin Wu, Jianhao Jiao +2

Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This pape…

cs.RO202419 cited

PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation

Xiangcheng Hu, Linwei Zheng, Jin Wu +7

Accurately generating ground truth (GT) trajectories is essential for Simultaneous Localization and Mapping (SLAM) evaluation, particularly under varying environmental conditions.…

cs.RO2023

PALoc: Robust Prior-assisted Trajectory Generation for Benchmarking

Xiangcheng Hu, Jin Wu, Jianhao Jiao +2

Evaluating simultaneous localization and mapping (SLAM) algorithms necessitates high-precision and dense ground truth (GT) trajectories. But obtaining desirable GT trajectories is…

cs.RO20221 cited

FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms

Jianhao Jiao, Hexiang Wei, Tianshuai Hu +10

Combining multiple sensors enables a robot to maximize its perceptual awareness of environments and enhance its robustness to external disturbance, crucial to robotic navigation. T…