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.
Access to complex graphical information is essential when connecting blind and visually impaired (BVI) people with the world. Tactile graphics readers enable access to graphical data through audio-tactile user interfaces (UIs), but these have yet to mature. A challenging task for blind people is locating specific elements–areas in detailed tactile graphics. To this end, we developed three audio navigation UIs that dynamically guide the user’s hand to a specific position using audio feedback. One is based on submarine sonar sounds, another relies on the target’s coordinate plan x and y-axis, and the last uses direct voice instructions. The UIs were implemented in the Tactonom Reader device, a new tactile graphic reader that enhances swell paper graphics with pinpointed audio explanations. To evaluate the effectiveness of the three different dynamic navigation UIs, we conducted a within-subject usability test that involved 13 BVI participants. Beyond comparing the effectiveness of the different UIs, we observed and recorded the interaction of the visually impaired participants with the different navigation UI to further investigate their behavioral patterns during the interaction. We observed that user interfaces that required the user to move their hand in a straight direction were more likely to provoke frustration and were often perceived as challenging for blind and visually impaired people. The analysis revealed that the voice-based navigation UI guides the participant the fastest to the target and does not require prior training. This suggests that a voice-based navigation strategy is a promising approach for designing an accessible user interface for the blind.
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