QIN Tong
Associate Professor
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolQIN Tong is an associate professor and researcher with a decade of experience at the intersection of aerial robotics, SLAM, visual-inertial odometry and autonomous driving, currently based in Shanghai. He holds a PhD from HKUST and has industrial R&D experience at Huawei and research internships at Oculus/Facebook focused on AR/VR and VIO. His open-source contributions to prominent projects like VINS-Mono, VINS-Fusion and A-LOAM show deep, practical expertise in estimator initialization, IMU/feature integration, loop closure and adapting lidar pipelines for 64-line sensors. He combines algorithmic rigor with system-level architecture work, having rewritten initialization logic and added robust relocalization and rolling-shutter support. Beyond papers and code, he brings production-minded insight from deploying perception stacks in autonomous driving settings. Colleagues will find him equally comfortable debugging low-level sensor math and shaping large-scale SLAM system designs.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Robotics Institute, 4.0, Doctor of Philosophy - PhD, Robotics Institute, 4.0 at Hong Kong University of Science and TechnologyBachelor's degree, control science and engineering, 4.0, Bachelor's degree, control science and engineering, 4.0 at Zhejiang University
Github Skills (22)
algorithm10
algorithms10
c-language10
integrate10
integrator10
imu10
parameter-estimation10
state-space10
estimate10
sensor-fusion10
lidar10
cprogramming-language10
point-cloud10
slam10
ros10
Programming languages (1)
Github contributions (5)
HKUST-Aerial-Robotics/VINS-Mono
May 2017 - Jan 2019
A Robust and Versatile Monocular Visual-Inertial State Estimator
Role in this project:
Back-end Developer & Algorithm Engineer
Back-end Developer & Algorithm EngineerContributions:64 commits, 2 PRs, 63 pushes in 1 year 8 months
Contributions summary:QIN primarily contributed to the core functionality of the visual-inertial state estimator, as indicated by the commit messages and code changes. Their work involved modifying configuration files, including parameters, and optimizing the frequency control of feature tracking. They also addressed bugs in initial SFM and IMU jacobian calculations, as well as added functionalities for loop closure and map merging. Furthermore, they improved fast relocalization and added support for rolling shutter cameras.
stereovisual-inertialintrinsicsstate-estimationinertial
HKUST-Aerial-Robotics/VINS-Fusion
Jan 2019 - Apr 2019
An optimization-based multi-sensor state estimator
Role in this project:Back-end Developer & System Architect
Back-end Developer & System ArchitectContributions:28 commits, 12 pushes, 2 comments in 2 months
Contributions summary:QIN primarily focused on modifying and improving the VINS-Fusion system. They made several path modifications and configuration adjustments. They rewrote initialization logic and added configurations, demonstrating contributions to core estimator functionality. The commits suggest a focus on system structure and configuration, potentially implying architectural understanding.
multi-sensorstereopybulletoptimizationsensor
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