| Proj No. | A1104-251 |
| Title | Multi-sensor Fusion for Pedestrian State Estimation and Tracking |
| Summary | Pedestrian state estimation using multi-sensor fusion holds significant potential for applications in autonomous navigation, smart cities, and safety-critical scenarios. Therefore, in this project, we propose to develop a multi-sensor fusion framework that leverages data from the camera and lidar to estimate human position and velocity. The system will be developed and tested within a controlled environment, such as a VICON room, to obtain ground-truth data for validation. Machine learning models, particularly neural networks, will be explored to learn motion constraints and enhance state estimation accuracy. Candidate should have familiarity with, or an interest in, ROS, Pytorch, and sensor fusion algorithms. Regular bi-weekly meetings will be conducted to ensure steady progress and collaboration. |
| Supervisor | Prof Xie Lihua (Loc:S2 > S2 B2C > S2 B2C 94, Ext: +65 67904524) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Internet of Things Laboratory (Loc: S1-B4c-14, ext: 5470/5475) |
| Single/Group: | Single |
| Area: | Intelligent Systems and Control Engineering |
| ISP/RI/SMP/SCP?: |