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dc.contributor.author | Sevastopoulos, Christos | |
dc.contributor.author | Acharya, Sneh | |
dc.contributor.author | Makedon, Fillia | |
dc.date.accessioned | 2023-11-14T18:45:05Z | |
dc.date.available | 2023-11-14T18:45:05Z | |
dc.date.issued | 2023-07 | |
dc.identifier.uri | http://hdl.handle.net/10106/31880 | |
dc.description.abstract | We present a method for extracting high-level semantic information through successful landmark detection using feature fusion
between RGB and depth information. We focus on the classification
of specific labels (open path, humans, staircases, doorways, obstacles) in the encountered scene, which can be a fundamental source
of information enhancing scene understanding, and acting towards
the safe navigation of the mobile unit. Experiments are conducted
using a manual wheelchair equipped with a stereo RGB-D camera
that captures image instances consisting of multiple labels before
fine-tuning on a pre-trained Vision Transformer (ViT). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ACM | en_US |
dc.subject | Wheelchair navigation, Multi-label classification | en_US |
dc.title | An RGB-D Fusion System for Indoor Wheelchair Navigation | en_US |
dc.type | Article | en_US |
dc.rights.license | Licensed under Creative Commons: CC BY 4.0 | |
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