A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications

DOES: A Deep Learning-based approach to estimate roll and pitch at sea

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES, a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method as a valid support to visual-based odometry techniques

Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)

Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE\u27s embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm