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Multimodal Terrain Classification for Off-Road Environments
Robust terrain understanding for the wild using depth-assisted semantic segmentation.
A depth-assisted, multimodal model for classifying unstructured off-road terrain.
- Built a robust terrain-classification model using depth-assisted semantic segmentation.
- Collected and generated a novel RGB-D + semantic-segmentation dataset of fully off-road / wild terrains.
- Used SOTA models (Intel's Dense Prediction Transformer and MiDaS) for dataset benchmarking.
- Designed a dual-stream RGB + Depth multimodal architecture with fusion-based feature combination.