# Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance
## 4 Bringing Forth Current R&D Challenges
### Verification and Mobile App  
At the moment the collected data is only verified by the volunteers. They have to visually inspect the collected data to make sure that it is correct, see Figure 9. We created a specialized android app for the volunteers. This app is used during the data collection stage. The app displays a 2D plan of each building floor and trajectories collected by the Visual SLAM algorithm are overlayed on this map. It is the volunteer's responsibility to verify that collected trajectories cover the whole building floor. Each volunteer would also be trained before performing the building mapping to make sure that they are able to verify the data. At the moment we do not use any automatic trajectory validation algorithms. In the future, this could be conducted using covered area validation algorithms.  
In the third operational modality, a BSVI person can use the mobile app to call volunteers who are familiar with that building or who were involved with the production of its routing data in the first modality. The web cloud server provides information about the current BSVI position on the chosen route (or last known location) and on that building's digitalized evacuation scheme, see Figure 10. A volunteer can help to recognize obstacles, read texts, and find a route in dead reckoning situations.  
Figure 8: Navigation instructions (voice and tactile output) and current location projected on the 2D evacuation scheme, using stored route data.  
At the current stage, we are working to achieve integrity of the overall system (modalities and modes), improve stability (failures occur in 36% cases), and accuracy of the SLAM algorithm (sometimes occur scaling issues) while building routes. We also have to resolve other technical issues such as more reliable detection of potential obstacles, stable web connection for data transfer, intuitive and user-friendly tactile-voice interface, minimizing time lags for vision-based real-time processing (currently about 2 sec time lag for vision-based recognition of objects), etc.  
Figure 10: Volunteers’ mobile app view (Modality#3: while assisting BSVI person in real-time).  
Figure 9: Mobile app view used by volunteers to record/delete routes and mark points of interest (Modality#1).  
Thus, our prototype is still in the development stage. Besides, as we stated in the title of the paper, this manuscript is mainly dedicated to shedding light on the innovative web-crowd outsourcing method for indoor routes' mapping and assistance. At the current stage, we are working to achieve integrity of the overall system (modalities and modes), improve stability (failures occur in 36% cases), and accuracy of the SLAM algorithm (scaling issues) while building routes. We also have to resolve a number of other technical issues such as effective detection of potential obstacles, stable web connection, intuitive and user-friendly tactile-voice interface, minimizing time lags for vision-based real-time processing (currently about 2 sec time lag for vision-based recognition of objects), etc.  
The literature review and patent DB analysis (see Section 1) indicate that the proposed web crowed assisted indoor navigation setup is unique and hardly comparable to other indoor navigation approaches that use infrastructural installations such as Wi-Fi routers, RFID tags, beamers, etc. Some live photos of the prototype development are presented in Figures 11 and 12.