3D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

The vision-aided Pedestrian Dead Reckoning (PDR) systems have become increasingly popular, thanks to the ubiquitous mobile phone embedded with several sensors. This is particularly important for indoor use, where other indoor positioning technologies require additional installation or body-attachment of specific sensors. This paper proposes and develops a novel 3D Passive Vision-aided PDR system that uses multiple surveillance cameras and smartphone-based PDR. The proposed system can continuously track users’ movement on different floors by integrating results of inertial navigation and Faster R-CNN-based real-time pedestrian detection, while utilizing existing camera locations and embedded barometers to provide floor/height information to identify user positions in 3D space. This novel system provides a relatively low-cost and user-friendly solution, which requires no modifications to currently available mobile devices and also the existing indoor infrastructures available at many public buildings for the purpose of 3D indoor positioning. This paper shows the case of testing the prototype in a four-floor building, where it can provide the horizontal accuracy of 0.16m and the vertical accuracy of 0.5m. This level of accuracy is even better than required accuracy targeted by several emergency services, including the Federal Communications Commission (FCC). This system is developed for both Android and iOS-running devices

Indoor Navigation on Smartphones

Today the main issue of absolute navigation system mainly consists of GNSS signal propagation problems in indoor areas and street canyons (also known as urban canyons). The existing solutions are unable to provide reliable location service in these areas thus a new approach is needed. Due to the rapid growth of smartphone market, wireless trans-mission networks (e.g. Wi-Fi, Bluetooth, WiMAX) have been gaining popularity over the last few years. These types of networks were originally designed for high-speed transmission of large data i.e. Internet access, but it can also be used for the navigation purposes. Moreover, during the evolution of smartphones, manufacturers started to add new types of self-contained sensors that have never been used in such a way before. Some of them like accelerometers, magnetometers and gyroscopes can be used to track movement and position of smartphone in space. During this research one of the latest and the most sensor-equipped smartphone was tested. Nexus 5, released by Google, was utilized as a testing platform for indoor tracking application based on self-contained sensors only. This implies a highly laborious and tedious work of manually collected training data and developing the corresponding indoor tracking application. The methods used in development process allows decreasing the overall development costs while notably improving the performance of the existing navigation systems. The implemented indoor navigation application utilizes pedestrian dead reckoning method that allows improving the accuracy of existing navigation methods. It can also be used separately in fingerprinting or SLAM process. This application was tested in several indoor areas with different location properties: narrow corridors, wide halls, tiny rooms. The corresponding application utilizes built-in accelerometers, magnetometers and step detectors to track the route. Magnetometer fluctuations were smoothed by using low-pass filter. The experiments showed the total positioning error between 7% and 14%, respectively. Tests of built-in step detector showed the average detection error of 0.5%, which is lower than existed solution can obtain. In general, the obtained positing error and performance improvement can be considered as immaterial but the results can be used as a platform for the future research

Indoor navigation systems based on data mining techniques in internet of things: a survey

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges