Real-time 2D–3D door detection and state classification on a low-power device

In this paper, we propose three methods for door state classification 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 offline, in low-powered computers as the Jetson Nano , in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet , real-time semantic segmentation algorithms such as, FastFCN , FC-HarDNet , SegNet and BiSeNet , the object detection algorithm, DetectNet and 2D object classification 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 classification algorithm running in real-time on a low-power device.