activity
20192022
most citedLIC-Fusion: LiDAR-Inertial-Camera Odometry

10 citations · 50 across the 11 of their papers we have counts for

collaborators

13 papers

cs.RO2022

Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems

Jiajun Lv, Xingxing Zuo, Kewei Hu +3

Accurate and reliable sensor calibration is essential to fuse LiDAR and inertial measurements, which are usually available in robotic applications. In this paper, we propose a nove…

cs.CV20214 cited

Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds

Xuemeng Yang, Hao Zou, Xin Kong +5

Outdoor scene completion is a challenging issue in 3D scene understanding, which plays an important role in intelligent robotics and autonomous driving. Due to the sparsity of LiDA…

cs.RO20211 cited

CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System

Jiajun Lv, Kewei Hu, Jinhong Xu +3

In this paper, we propose a highly accurate continuous-time trajectory estimation framework dedicated to SLAM (Simultaneous Localization and Mapping) applications, which enables fu…

cs.CV20212 cited

SSC: Semantic Scan Context for Large-Scale Place Recognition

Lin Li, Xin Kong, Xiangrui Zhao +2

Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information…

cs.RO20213 cited

SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure

Lin Li, Xin Kong, Xiangrui Zhao +4

LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue. With the development of 3D semantic segmentatio…

cs.CV202010 cited

FlowMOT: 3D Multi-Object Tracking by Scene Flow Association

Guangyao Zhai, Xin Kong, Jinhao Cui +2

Most end-to-end Multi-Object Tracking (MOT) methods face the problems of low accuracy and poor generalization ability. Although traditional filter-based methods can achieve better…