Contact
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Available | |
Project | Lazy Camera Calibration Toolbox Development (Yanyan Li, Federico Tombari) |
Running |
Finished | |
Project | Reconstructing the MI. Building in a Day (Yanyan Li, Federico Tombari) |
Master Thesis | Memory-enhanced Categrory-Level Pose Estimation (Yan DI Yanyan Li, Federico Tombari) |
Master Thesis | indoorSLAM: Robust RGB-D SLAM based on Plane-Line-Point features for indoor scenes (Yanyan Li, Federico Tombari) |
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) |
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.
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.
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.
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.
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Title: | M.Sc. |
Circumference of your head (in cm): | |
Firstname: | Yanyan |
Middlename: | |
Lastname: | Li |
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Nationality: | China |
Languages: | English |
Groups: | Computer Vision |
Expertise: | Computer Vision |
Position: | Scientific Staff |
Status: | Active |
Emailbefore: | yanyan.li.camp |
Emailafter: | gmail.com |
Room: | 03.13.035 |
Telephone: | 089 289 19345 |
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