Mingyang Li - Department of Electrical and Computer Engineering

Mingyang Li
C ONTACT
I NFORMATION
Ph.D. Candidate
Department of Electrical Engineering
University of California, Riverside
Riverside, CA, 92521, USA
R ESEARCH
I NTERESTS
Computer Vision and Robotics
• Real-time 6D pose tracking of mobile devices using visual and inertial sensing (Project
website: www.ee.ucr.edu/∼mli/VIO_demo).
• Autonomous vehicle navigation, simultaneous localization and mapping, using odometry,
inertial measurements, and vision information.
• Self-calibration of camera and inertial measurement units, sensor fusing and synchornization.
• Estimator design and performance optimization of localization systems, using Kalman filter,
particle filter, and incremental batch-optimization approach.
E DUCATION
University of California, Riverside, Riverside, CA
Mobile: +1-951-333-9817
E-mail: [email protected]
Web: www.ee.ucr.edu/∼mli
Ph.D., Department of Electrical Engineering,
Sep. 2009 - Present
• Thesis Topic: High-precision, real-time, vision-aided inertial navigation on resourceconstrained systems
• Adviser: Professor Anastasios Mourikis
M.Sc., Department of Electrical Engineering,
Sep. 2009 - Mar. 2012
University of Electronic Science and Technology of China, Chengdu, China
B.Eng., Department of Automation Engineering,
Sep. 2005 - Jul. 2009
• Senior Project: Sliding Model Control for Robot Manipulators
P ROFESSIONAL
E XPERIENCE
Qualcomm Research, San Diego, CA
July 2013 - Sep. 2013
System Engineer Intern
• Derived visual-inertial SLAM algorithms for indoor localization and augmented reality
applications
• Reduced the computational cost for indoor localization systems by optimizing both the
mathematical formulations and software implementations.
University of California, Riverside, Riverside, CA
Graduate Research Assistant
Proposed, Derived, and Implemented Algorithms for:
Sep. 2009 - Present
• High-precision, real-time pose tracking for consumer-grade mobile devices using inertial and visual sensing in unknown environments, achieving errors less than 0.5% of
traveled distance over kilometers.
• Vision-based localization with rolling shutter cameras.
• Intrinsic/extrinsic camera and IMU calibration, for visual-inertial navigation systems.
• Estimator initialization for vision-aided inertial navigation systems without requiring
any prior estimate.
• EKF-based, high-precision, consistent vision-aided inertial navigation, by enforcing the
correct observability properties for the linearized systems.
• Computation optimization of localization algorithms by using computational complementary algorithms and learning the environmental information.
• Particle filter based localization system by fusing measurements from odometry and
monocular camera.
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AWARDS
Qualcomm Research
• Roberto Padovani Scholarship (Outstanding intern award), 2013
IEEE Robotics and Automation Society
• Student travel scholarship, ICRA, 2013
National Science Foundation
• Student travel scholarship, ICRA, 2014
National ICT Australia Ltd
• NICTA Student Fellowship, Robotics: Science and Systems, 2012
University of California, Riverside
• UCR Graduate Research Assistant Scholarship, 2010 -2013
• UCR Graduate Teaching Assistant Scholarship, 2011 -2013
• UCR Earle C. Anthony Graduate Student Travel Awards, 2012
• UCR Dean’s Distinguished Fellowship, 2009
University of Electronic science and technology of China
• The Second Class Scholarship, 2009
• National Scholarship of China, 2008
• The First Class Scholarship, 2007
R EFEREED
J OURNAL
P UBLICATIONS
[J1] M. Li, A.I. Mourikis, “Real-time Visual-Inertial Localization for Resource-Constrained
Systems", International Journal of Robotics Research. In preparation.
[J2] M. Li, A.I. Mourikis, “Vision-aided Inertial Navigation with Rolling-Shutter Cameras".
International Journal of Robotics Research. Accepted,
[J3] M. Li, A.I. Mourikis, “Online Temporal Calibration for Camera-IMU Systems: Algorithm
Design and Observability Analysis", International Journal of Robotics Research. 33(6),
May 2014.
[J4] M. Li, A.I. Mourikis, “High-Precision, Consistent EKF-based Visual-Inertial Odometry",
International Journal of Robotics Research. 32(6), pp. 690-711, May 2013.
C ONFERENCE
P UBLICATIONS
[C1] M. Li, A.I. Mourikis, “High-fidelity Sensor Modeling and Self-Calibration in Visionaided Inertial Navigation,” Proceedings of the IEEE International Conference on Robotics
and Automation (ICRA), HongKong, China, 2014, Accepted
[C2] M. Li, Byung Hyung Kim, A.I. Mourikis, “Real-time Motion Tracking on a Cellphone
using Inertial Sensing and a Rolling-Shutter Camera,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kalsruhe, Germany, May
2013
[C3] M. Li, A.I. Mourikis, “3-D Motion Estimation and Online Temporal Calibration for CameraIMU Systems,” Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA), Kalsruhe, Germany, May 2013
[C4] M. Li, A.I. Mourikis, “Vision-aided Inertial Navigation for Resource-Constrained Systems,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), Algarve, Portugal, October 2012
[C5] M. Li, A.I. Mourikis, “Optimization-Based Estimator Design for Vision-Aided Inertial
Navigation ,” Proceedings of the Robotics: Science and Systems Conference (RSS),
Sydney, Australia, July 2012
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[C6] M. Li, A.I. Mourikis, “Improving the Accuracy of EKF-based Visual-Inertial Odometry,”
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),
St Paul, MN, USA, May 2012
[C7] T. Yap, M. Li, A.I. Mourikis, C.R. Shelton, “A Particle Filter for Monocular Vision-aided
Odometry,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 2011
T ECHNICAL
R EPORTS AND
PATENTS
[R1] A.I. Mourikis, M. Li, “Method for Motion Estimation with a Scanning Camera,” US
Provisional Patent Application, 2013
[R2] M. Li, A.I. Mourikis, “3D-Motion Estimation and Online Temporal Calibration for CameraIMU Systems,” US Provisional Patent Application, 2013
[R3] M. Li, A.I. Mourikis, “Real-Time Pose Estimation System Using Inertial and Feature
Measurements,” US Provisional Patent Application, 2013
[R4] M. Li, A.I. Mourikis, “Consistency of EKF-based Visual-Inertial Odometry,” Technical
Report, May, 2012
P OSTER
PRESENTATIONS
[P1] M. Li, A.I. Mourikis, “Real time, large-scale visual-inertial navigation for mobile devices”, at IEEE conference on Computer Vision and Pattern Recognition, Portland,
June, 2013
[P2] M. Li, Byung Hyung Kim, A.I. Mourikis, “Real-time Motion Tracking on a Cellphone
using Inertial Sensing and a Rolling-Shutter Camera,” at IEEE International Conference
on Robotics and Automation (ICRA), Kalsruhe, Germany, May 2013
[P3] M. Li, A.I. Mourikis, “3-D Motion Estimation and Online Temporal Calibration for CameraIMU Systems,” at IEEE International Conference on Robotics and Automation (ICRA),
Kalsruhe, Germany, May 2013
[P4] M. Li, A.I. Mourikis, “Optimization-Based Estimator Design for Vision-Aided Inertial
Navigation ,” at Robotics: Science and Systems Conference (RSS), Sydney, Australia,
July 2012
T ECHNICAL S KILLS •
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S OFTWARE SKILLS
Computer vision: Multiple view geometry, structure from motion, visual odometry.
Estimation: Convex optimization, MAP, MLE, Kalman/particle filtering.
Localization: Map-based localization, simultaneous localization and mapping.
Sensor fusion: Sensor calibration, sensor synchronization .
Image processing: Feature detection and matching, video stabilization, image segmentation.
• Programming: C/C++, JAVA, Multi-threaded programming, Matlab, Latex.
• Toolbox: OpenCV, OpenGL, MS Office, CVX.
• Platform: Windows, Linux, Android.
S ELECTED
C OUSEWORK
Probabilistic Graphical Models, Advanced Robotics, Advanced Computer Vision, Information
Theory, State and Parameter Estimation, Stochastic process, Linear Systems, Nonlinear Systems and Control, Adaptive Signal Processing, Optimal Control, Convex Optimization.
M ORE
I NFORMATION
More information and auxiliary documents can be found at
http://www.ee.ucr.edu/~mli/.
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