Jiarong Lin

Jiarong Lin

Ph.D. candidate in Robotics🤖

The University of Hong Kong

    I am Jiarong Lin (林家荣), graduated from The University of Hong Kong (HKU) with a Ph.D. degree in robotics. My research interests lie in the areas of Simultaneous Localization and Mapping (SLAM), Multi-Sensor Fusion, and 3D Reconstruction. I have a proven track record of producing high-quality research that is the first author of 9 paper, including 2×T-RO, 1×T-PAMI (in revision), 1×RA-L journal, and 3×ICRA, 2×IROS conference paper. (see my publication list for details).
    In addition to my academic pursuits, I am also an active open-source contributor😊. I have been greatly benefited from open-source communities, and correspondingly, I have dedicated my contributions to this community as well. I have made all the code for my publications available on GitHub, where it has received over 7.8k stars⭐ from the community. Some of my most popular works include R3LIVE (★1.8k), FAST-LIO (★2.1k), loam-livox (★1.4k), R2LIVE (★0.7k), and ImMesh🆕(★0.5k).
    I am dedicated to producing high-quality research and making meaningful contributions to both academics and industry.

Skills

LiDAR
LiDAR SLAM

Top LiDAR SLAMer in both communities of academics and industry

sensor_fusion
Multi-Sensor Fusion

Expert in tightly-coupled LiDAR-Inertial, Visual-Inertial, and LiDAR-Inertial-Visual fusion

3D_rec
3D reconstruction

Expert in point cloud mapping, triangle facets meshing and texturing

drone
Robotics

Master in drone assembling, UAV motion planning and control

c++
C/C++

With experiments of over 12+ years

CAD
CAD

Master in 3D-printing, OpenGL, Unreal-Engine, Solidwork, and 3DS MAX

Experience

 
 
 
 
 
The university of Hong Kong (HKU)
Ph.D. in Robotics
The university of Hong Kong (HKU)
January 2019 – October 2023 Hong Kong

Research interests include:

  • LiDAR SLAM
  • Sensor Fusion
  • 3D reconstruction
 
 
 
 
 
Hong Kong University of Science and Technology (HKUST)
Ph.D. student
Hong Kong University of Science and Technology (HKUST)
August 2018 – January 2019 Hong Kong

Research interests include:

  • UAV motion planning and control
  • Deep reinforcement learning
 
 
 
 
 
DaJiang Innovation (DJI)
Computer Vision Engineer
DaJiang Innovation (DJI)
August 2015 – July 2018 Shen Zhen

In DJI, I worked for:

 
 
 
 
 
University of Electronic Science and Technology of China (UESTC)
Bachelor of Engineering
University of Electronic Science and Technology of China (UESTC)
September 2011 – July 2015 Cheng Du

Specialization:

  • Optical Information Science and Technology

Recent
Activities

Gave a talk on "Simultaneous Localization and Mapping with Multi-sensor Fusion" at shenlanxueyuan.com (Audiences: 13,000+).
Invited by shenlanxueyuan.com, I give an online talk on “Simultaneous Localization and Mapping with Multi-sensor Fusion (基于多传感器融合的定位和建图系统)”. In this talk, I shared my researches in my Ph.D. studies.
Jiarong Lin
Jan 16, 2023 1 min read
Slides Talk
Gave a talk on "Simultaneous Localization and Mapping with Multi-sensor Fusion" at shenlanxueyuan.com (Audiences: 13,000+).
Release of work "ImMesh: An Immediate LiDAR Localization and Meshing Framework"
ImMesh is a novel LiDAR(-inertial) odometry and meshing framework, which takes advantage of input of LiDAR data, achieving the goal of simultaneous localization and meshing in real-time.
Jiarong Lin, Chongjiang Yuan, Yixi Cai, Haotian Li, Yuying Zou, Xiaoping Hong, Fu Zhang
Jan 13, 2023 1 min read
Release of work "ImMesh: An Immediate LiDAR Localization and Meshing Framework"

My popular open-source projects

*
All ImMesh R3LIVE FAST-LIO loam_livox R2LIVE
🆕ImMesh: An Immediate LiDAR Localization and Meshing Framework
ImMesh is a novel LiDAR(-inertial) odometry and meshing framework, which takes advantage of input of LiDAR data, achieving the goal of simultaneous localization and meshing in real-time. ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster.
PDF Cite Code on Github (★ 0.2K🆕) Video
🆕ImMesh: An Immediate LiDAR Localization and Meshing Framework
R$^3$LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
R$^3$LIVE is a versatile and well-engineered system toward various possible applications, which can not only serve as a SLAM system for realtime robotic applications but can also reconstruct the dense, precise, RGB-colored 3D maps for applications like surveying and mapping. In addition, we have developed a series of offline utilities for reconstructing and texturing meshes for various of 3D applications.
PDF Cite Code on Github (★ 1.4K) Video-1 Video-2 Hardware-Design
R$^3$LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
FAST-LIO2: Fast Direct LiDAR-inertial Odometry
FAST-LIO (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. Our package addresses many key issues: 1) Fast error state iterated Kalman filter (ESIKF) for odometry optimization. 2) Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz LiDAR rate. 3) Without the need for feature extraction, FAST-LIO2 can support many types of LiDAR including spinning (Velodyne, Ouster) and solid-state (Livox Avia, Horizon, MID-70) LiDARs, and can be easily extended to support more LiDARs.
PDF Cite Code on Github (★ 1.4K) Video
FAST-LIO2: Fast Direct LiDAR-inertial Odometry
R$^2$LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
R$^2$LIVE is a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses the measurement from the LiDAR, inertial sensor, visual camera to achieve robust, accurate state estimation. Taking advantage of measurement from all individual sensors, our algorithm is robust enough to various visual failure, LiDAR-degenerated scenarios, and is able to run in real time on an on-board computation platform, as shown by extensive experiments conducted in indoor, outdoor, and mixed environment of different scale.
PDF Cite Code on Github (★ 0.6K) Video Hardware-Design
R$^2$LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
LOAM_Livox: A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR
Loam-Livox is a robust, low drift, and real time odometry and mapping package for Livox LiDARs, significant low cost and high performance LiDARs that are designed for massive industrials uses. Our package address many key issues: feature extraction and selection in a very limited FOV, robust outliers rejection, moving objects filtering, and motion distortion compensation. In addition, we also integrate other features like parallelable pipeline, point cloud management using cells and maps, loop closure, utilities for maps saving and reload, etc. To know more about the details, please refer to our related paper:)
PDF Cite Code on Github (★ 1.2K) Video-1 Video-2 Hardware-Design
LOAM_Livox: A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR

Featured Publications

Quickly discover relevant content by filtering publications.
Jiarong Lin, Chongjiang Yuan, Yixi Cai, Haotian Li, Yuying Zou, Xiaoping Hong, Fu Zhang (2023). ImMesh: An Immediate LiDAR Localization and Meshing Framework. Under review.

PDF Cite Code on Github (★ 0.2K🆕) Video

Chongjiang Yuan, Jiarong Lin, Zuhao Zou, Xiaoping Hong, Fu Zhang (2022). STD: Stable Triangle Descriptor for 3D place recognition. Accepted to ICRA2023.

PDF Cite Code on Github (★ 0.1K🆕) Video

Jiarong Lin, Fu Zhang (2022). R$^3$LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator. Under review.

PDF Cite Code on Github (★ 1.4K) Dataset Video Hardware-Design

Jiarong Lin, Fu Zhang (2022). R$^3$LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package. Accepted to ICRA2022.

PDF Cite Code on Github (★ 1.4K) Dataset Video-1 Video-2 Hardware-Design

Wei Xu, Yixi Cai, Dongjiao He, Jiarong Lin, Fu Zhang (2021). FAST-LIO2: Fast Direct LiDAR-inertial Odometry. Accepted to T-RO2022.

PDF Cite Code on Github (★ 1.4K) Video

Jiarong Lin, Chunran Zheng, Wei Xu, Fu Zhang (2021). R$^2$LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping. Accepted to RA-L2022.

PDF Cite Code on Github (★ 0.6K) Video Hardware-Design

Jiarong Lin, Xiyuan Liu, Fu Zhang (2021). A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs. Accepted to IROS2021.

PDF Cite Code on Github (★ 0.2K) Video

Jiarong Lin, Fu Zhang (2020). Loam_livox: A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR. Accepted to ICRA2020.

PDF Cite Code on Github (★ 1.2K) Video

Jiarong Lin, Luqi Wang, Fei Gao, Shaojie Shen, Fu Zhang (2020). Flying through a narrow gap using neural network: an end-to-end planning and control approach. Accepted to IROS2020.

PDF Cite Code on Github (★ 33) Video

Publications

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Xinyi Chen, Boyu Zhou, Jiarong Lin, Yichen Zhang, Fu Zhang, Shaojie Shen (2022). Fast 3D Sparse Topological Skeleton Graph Generation for Mobile Robot Global Planning. Accepted to IROS2022.

PDF Cite Video

Fanze Kong, Xiyuan Liu, Benxu Tang, Jiarong Lin, Yunfan Ren, Yixi Cai, Fangcheng Zhu, Nan Chen, Fu Zhang (2022). MARSIM: A light-weight point-realistic simulator for LiDAR-based UAVs. Accepted to RA-L2023.

PDF Cite Video

Wenliang Gao, Jiarong Lin, Fu Zhang, Shaojie Shen (2019). A Screen-Based Method for Automated Camera Intrinsic Calibration on Production Lines. Accepted to CASE2019.

PDF

See all publications

Talks

Jan 16, 2023 8:00 PM — 11:00 PM Online talk
Simultaneous Localization and Mapping with Multi-sensor Fusion
Simultaneous Localization and Mapping with Multi-sensor Fusion

Invited by shenlanxueyuan.com, I give an online talk on “Simultaneous Localization and Mapping with Multi-sensor Fusion”. In this talk, I shared my researches in my Ph.D. studies.

Slides Talk

Popular Topics

3D reconstruction Calibration FAST-LIO ImMesh LiDAR SLAM LiDER-Inertial LiDER-Inertial-Visual loam_livox Loop closure Loop detection Motion Planning Multi-sensor Fusion R2LIVE R3LIVE Reinforcement Learning UAV control VTOL

Contact

  • ziv.lin.ljrATgmail.com
  • +852 5624 9033
  • LG-02, Haking Wong Building, Pok Fu Lam Road,
    The university of Hong Kong (HKU),
    Hong Kong SAR, China
  • 10:00-AM to 22:00-PM
  • ziv-lin
  • ziv.lin
  • Search for id: Ziv-Lin-LJR