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November 30, 2024
Master Thesis
Title
Real World Semantic Occupancy Prediction for Advanced Air Mobility
Other Title
Vorhersage semantischer Occupancy Grids im Kontext von Realdaten der Advanced Air Mobility
Abstract
Perception of the environment represents a foundational yet intricate challenge for autonomous robotic systems. Recent computer vision and deep learning advancements have established semantic occupancy grids as a standard geometric scene perception and semantic understanding framework. Numerous deep learning models have been trained and validated extensively on benchmark datasets, significantly augmenting autonomous navigation, path planning, and decision-making capabilities. However, most of these datasets are tailored specifically for terrestrial autonomous driving applications, with very limited consideration of aerial mobility scenarios.
A novel semantic occupancy dataset has been introduced in this research, specifically designed for aerial mobility, and developed using a novel data generation pipeline that relies solely on monocular RGB aerial imagery. The pipeline integrates 3D reconstruction, semantic fusion, and voxel densification to produce dense voxel-based representations of the environment enriched with visibility-aware semantic labels. Annotation time, which traditionally requires weeks per scene, is significantly reduced to mere hours through its autonomous operation with minimal manual intervention. [...]
A novel semantic occupancy dataset has been introduced in this research, specifically designed for aerial mobility, and developed using a novel data generation pipeline that relies solely on monocular RGB aerial imagery. The pipeline integrates 3D reconstruction, semantic fusion, and voxel densification to produce dense voxel-based representations of the environment enriched with visibility-aware semantic labels. Annotation time, which traditionally requires weeks per scene, is significantly reduced to mere hours through its autonomous operation with minimal manual intervention. [...]
Thesis Note
Deggendorf, FH, Master Thesis, 2024
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