In this paper, we propose three methods for door state classifcation with the goal to improve robot navigation in indoor
spaces. These methods were also developed to be used in other areas and applications since they are not limited to door
detection as other related works are. Our methods work ofine, in low-powered computers as the Jetson Nano, in real-time
with the ability to diferentiate between open, closed and semi-open doors. We use the 3D object classifcation, PointNet,
real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection
algorithm, DetectNet and 2D object classifcation networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset
with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online.
All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer.
We conclude that it is possible to have a door classifcation algorithm running in real-time on a low-power device.info:eu-repo/semantics/publishedVersio
