Genghang Zhuang
Technical University of Munich
Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll)
Postal address
Postal:
Boltzmannstr. 3
85748 Garching b. München
- Office hours: by appointment
- Room: 5607.03.059
- genghang.zhuang@tum.de
Curriculum Vitae
Genghang Zhuang is currently a doctoral student at Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich. He received his M.Eng degree in Software Engineering at Sun Yat-sen University, China, in 2019, and his B.Eng degree in Software Engineering at the same university in 2017.
His research interests include perception and planning in autonomous driving, especially with LiDAR sensors and biologically inspired methods.
| 2019.10 - present | Research Assistant in Informatics 6, Technical University of Munich |
| 2017.9 - 2019.6 | M.Eng. at School of Computer Science and Engineering, Sun Yat-sen University, China |
| 2013.8 - 2017.6 | B.Eng. at School of Computer Science and Engineering, Sun Yat-sen University, China |
Thesis Topics
- [MA/BA] Brain-inspired Localization and Mapping based on LiDAR Sensor
- [MA/BA] Spiking Neural Network for Autonomous Navigation based on LiDAR Sensor
- [MA/BA] Biologically-inspired Perception for Autonomous Vehicles based on LiDAR Sensor
If you are interested in one of the topics, please feel free to contact him.
Teaching
Winter term 2021/22
| Course no. | Title | Dates | Duration | Type | Lecturer (assistant) |
|---|---|---|---|---|---|
| 0000000590 | Praktikum - Development of Biologically-inspired and Conventional AI Methods for Autonomous Robots (IN2106, IN0012, IN4309) |  | 6 | PR |
Demos
• LiDAR based SLAM and Navigation for Indoor Environments
Publications
- Fusion-based Feature Attention Gate Component for Vehicle Detection based on Event Camera. IEEE Sensors Journal, 2021, 1-1 more…
- Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network. IEEE Transactions on Neural Networks and Learning Systems, 2021 more…
- A Real-Time Embedded Localization in Indoor Environment Using LiDAR Odometry. In: Communications in Computer and Information Science. Springer Singapore, 2018 more…