YanyanLi

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Yanyan Li

Yanyan

Contact

PhD student, Technische Universität München
Fakultät für Informatik
Boltzmannstr. 3
85748 Garching b. München
Germany
Room: MI 03.13.035
Email: yanyan.li@tum.de

News

  • May 26 (2021): a stereo matching paper pused to arXiv, accepted to RA-L/IROS' 21
  • March 1 (2021): two papers accepted to ICRA' 21
  • Sep 9 (2020): 1 Paper accepted to RA-L
  • Jul 2 (2020): 1 Paper accepted to RA-L/IROS' 20

Student Project

Feel free to contact me if you're interested in a bachelor's or master's thesis related to my research areas.

Available
ProjectLazy Camera Calibration Toolbox Development
(Yanyan Li, Federico Tombari)

Running

Finished
ProjectReconstructing the MI. Building in a Day
(Yanyan Li, Federico Tombari)
Master ThesisMemory-enhanced Categrory-Level Pose Estimation
(Yan DI Yanyan Li, Federico Tombari)
Master ThesisindoorSLAM: Robust RGB-D SLAM based on Plane-Line-Point features for indoor scenes
(Yanyan Li, Federico Tombari)

Teaching

Research topics

  • Camera pose estimation for robots and autonomous-driving cars based on geometric and learning methods.
  • Geometric prediction and understanding.

Publications

2020
Y. Li, N. Brasch, Y. Wang, N. Navab, F. Tombari
Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments
International Conference on Intelligent Robots and Systems (IROS), October 25-29, 2020. Las Vegas, USA.
The first two authors contributed equally.
(bib)

TOPIC: Tracking, Reconstruction and Scene Understanding for Indoor Environments

The basic idea of this topic is to propose a dense-accuracy-robust-efficient-semantic (Dares) SLAM system for indoor scenes, which makes uses of RGB-D/Stereo/Monocular and IMU sensors. Benefiting from the constraints between common features of indoor environments and new optimization strategies, we pursue to achieve a high-level accuracy. Thanks to those outstanding contributors, it is cheap to obtain a dense reconstruction map based on estimated camera poses. But how to make the map lighter and higher quality still requires more contributions. Deep neural networks have achieved impressive performance in more and more computer vision topics, so how to combine those SOTA methods with geometric-based SLAM systems to help robots understand unknown scenes is also a necessary and interesting section in this topic.

Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments[slides]

camp-iros.png

We present a low-drift monocular SLAM method targeting indoor scenarios. Our approach decouples rotation and translation estimation of the tracking process to reduce the long-term drift in indoor environments.

Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry [code] [video]

camp-iros.png

This work builds an efficient and robust parametrization for co-planar points&lines, which leverages specific geometric constraints to improve camera pose optimization in terms of both efficiency and accuracy.

RGB-D SLAM with Structural Regularities [code] [video]

RGBdSLAM.png

This work builds a SLAM architecture for RGB-D sensors, which makes use of structure regularities of indoor scenes and provides accurate pose estimation and compact mesh on a CPU.

ManhattanSLAM?: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames

ManhattanSLAM.png

This work builds a RGB-D SLAM system for general indoor scenes.


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Title: M.Sc.
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Firstname: Yanyan
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Nationality: China
Languages: English
Groups: Computer Vision
Expertise: Computer Vision
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Room: 03.13.035
Telephone: 089 289 19345
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Edit | Attach | Refresh | Diffs | More | Revision r1.40 - 23 Jul 2021 - 16:51 - YanyanLi

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