Drone Systems for Factory Security and Surveillance

Nowadays, when preparations and implementations are under way for smart cities, the use of drone systems in the safety of factories has come to the fore. Factories and industrial areas are complex systems. Physical control is essential for their optimal and safe operation. Most of the inspections can be performed with the use of human resources. However, efforts should be made to minimize the human factor in order to make the system as automated and optimized as possible. Pre-programmed routine tasks can be performed by drones, both indoors and outdoors. Dedicated drones are already in use around industrial facilities, primarily for facility protection. However, in enclosed halls, it is not easy to provide these tools with routine tasks, because indoor labor – material handling, reconnaissance, and accident-free transport – requires orientation. Besides the production lines and inside warehouse buildings, drones are already commonly used to perform smaller tasks, but the goal is to ensure the right ratio in the human-machine relationship. In their technical implementation, modern drones are assisted by various sensor systems (lidar, ultrasound, camera) that they are equipped with. This article presents the application of task-specific drones in industrial areas, both indoors and outdoors

Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform

Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided

Collaborative navigation as a solution for PNT applications in GNSS challenged environments: report on field trials of a joint FIG / IAG working group

PNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10–15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques – so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled ‘Ubiquitous Positioning Systems’ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ‘network’ or ‘neighbourhood’ of users is to be positioned / navigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users

PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146

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

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context

Barometric Assistance Service for Assisted GNSS Receivers

In the age of information the ability to navigate persons and equipment has become increasingly important. A rising number of applications and services depend on the precise positioning that is provided by global satellite positioning systems such as the GPS. However, most people using satellite-based positioning services are living in the most challenging surroundings for the satellite positioning systems - densely populated cities. Fundamentally, satellite navigation is based on distance measurements from the receiver to satellite vehicles in orbit of the Earth. The receiver determines its location - latitude, longitude, and elevation - and the system time using the satellite positioning system. The determined location is only an estimate: residual errors induce inaccuracies to the determination process. At worst, the receiver may not be able to determine its position if not enough signals could be acquired. The performance of the receiver could be greatly improved if one or more of the geographic coordinates or the precise time could be obtained from another source with smaller error. One such source is Earth's atmospheric pressure which is relative to the altitude and from which a receiver can deduce its altitude if a reference pressure level is known. To address the problem of unavailability and inaccuracy of positioning in urban environment, a barometric assistance service was designed and a respective software application was implemented. The implemented assistance service generates continuously time and location-dependent reference pressure data from high resolution weather forecasts that are calculated by the Finnish Meteorological Institute. Receivers with the barometric sensor can download the assistance data from the service and utilize it to determine the current barometric altitude consistently and more accurately than a conventional receiver. Barometric altitude measurements improve the availability of the positioning service and reduce the time required for the first position estimate by decreasing the number of required satellite measurements

Improving the Vertical Accuracy of Indoor Positioning for Emergency Communication

The emergency communication systems are undergoing a transition from the PSTN-based legacy system to an IP-based next generation system. In the next generation system, GPS accurately provides a user's location when the user makes an emergency call outdoors using a mobile phone. Indoor positioning, however, presents a challenge because GPS does not generally work indoors. Moreover, unlike outdoors, vertical accuracy is critical indoors because an error of few meters will send emergency responders to a different floor in a building. This paper presents an indoor positioning system which focuses on improving the accuracy of vertical location. We aim to provide floor-level accuracy with minimal infrastructure support. Our approach is to use multiple sensors available in today's smartphones to trace users' vertical movements inside buildings. We make three contributions. First, we present the elevator module for tracking a user's movement in elevators. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Second, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users' foot steps, our stairway module uses a novel landing counting technique. Third, we present a hybrid architecture that combines the sensor-based components with minimal and practical infrastructure. The infrastructure provides initial anchor and periodic corrections of a user's vertical location indoors. The architecture strikes the right balance between the accuracy of location and the feasibility of deployment for the purpose of emergency communication

User Activity Recognition Method based on Atmospheric Pressure Sensing

Abstract Several studies have been conducted on context recognition as well as hobby and preference extraction by analyzing the data obtained from the sensors in a smartphone. As a smartphone component, a barometer is expected to be useful for activity recognition because of its low power consumption. In this work, we propose an activity recognition method of classifying a user's state into indoor and outdoor states and using a barometer at each state. In the proposed method, the floor of a building on which a user is located is estimated by determining atmospheric pressure variations sensed in the indoor state, and the user's location is estimated by determining atmospheric pressure variations according to the user movement along a track in the outdoor state. In particular, this paper delineates the method of estimating the current floor on which the user is located. We confirmed that it is possible to closely estimate the current floor of the building in the case of user movement among eighteen floors