Please use this identifier to cite or link to this item:
http://repository.iiitd.edu.in/xmlui/handle/123456789/2171| Title: | Multi-modal sensor fusion for geometric and semantic scene understanding with graphs |
| Authors: | Rao, Navvrat Anand, Saket (Advisor) |
| Keywords: | Autonomous Driving Deep Fusion Early Fusion Multi-Modal Sensor Fusion Occupancy Grid |
| Issue Date: | 26-Apr-2024 |
| Publisher: | IIIT-Delhi |
| Abstract: | This thesis project, titled Multi-Modal Sensor Fusion for Geometric and Semantic Scene Understanding with Graphs, aims to highlight the constraints inherent in sensors such as LiDAR and cameras and highlight current tools and algorithms for clear interpretation of the environment to occupancy grid. We propose a solution using late fusion for these sen sors, then apply the Kalman filter to the fused data to estimate the detected obstacle’s state. The information derived from this process will then be integrated into the occupancy grid for enhanced accuracy and navigational utility. |
| URI: | http://repository.iiitd.edu.in/xmlui/handle/123456789/2171 |
| Appears in Collections: | Year-2024 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Multi_Modal_Sensor_Fustion_for_Geometric_and_Semantic_Understanding (12) - Navvrat Rao.pdf Restricted Access | 3.72 MB | Adobe PDF | View/Open Request a copy |
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