Using Smartphone Sensors for Localization in BAN

Nowadays, various sensors are embedded in smartphone, making it a great candidate for localization applications. In this chapter, we explored and listed the localization sensors in smartphone, their characteristics, platforms, coordinate system and how they can be used in BAN. These sensors can be roughly divided into three types: physical IMU sensors (accelerometer, gyroscope and magnetometer), virtual IMU (gravity, step counter and electronic compass) and the environmental sensors (barometer, proximity and other miscellaneous). By applying different mathematical methods, the location of the target or the users can be calculated and used for further use, such as navigation, healthcare or military purpose

An Indoor and Outdoor Navigation System for Visually Impaired People

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented

Vision-based indoor localization via a visual slam approach

With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image. © Authors 2019