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Multi-modal sensor fusion for geometric and semantic scene understanding with graphs

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dc.contributor.author Rao, Navvrat
dc.contributor.author Anand, Saket (Advisor)
dc.date.accessioned 2026-09-18T11:30:47Z
dc.date.available 2026-09-18T11:30:47Z
dc.date.issued 2024-04-26
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/2171
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher IIIT-Delhi en_US
dc.subject Autonomous Driving en_US
dc.subject Deep Fusion en_US
dc.subject Early Fusion en_US
dc.subject Multi-Modal Sensor Fusion en_US
dc.subject Occupancy Grid en_US
dc.title Multi-modal sensor fusion for geometric and semantic scene understanding with graphs en_US
dc.type Other en_US


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