Enhanced Indoor Localization System based on Inertial Navigation

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues

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

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible

Map matching by using inertial sensors: literature review

This literature review aims to clarify what is known about map matching by using inertial sensors and what are the requirements for map matching, inertial sensors, placement and possible complementary position technology. The target is to develop a wearable location system that can position itself within a complex construction environment automatically with the aid of an accurate building model. The wearable location system should work on a tablet computer which is running an augmented reality (AR) solution and is capable of track and visualize 3D-CAD models in real environment. The wearable location system is needed to support the system in initialization of the accurate camera pose calculation and automatically finding the right location in the 3D-CAD model. One type of sensor which does seem applicable to people tracking is inertial measurement unit (IMU). The IMU sensors in aerospace applications, based on laser based gyroscopes, are big but provide a very accurate position estimation with a limited drift. Small and light units such as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very popular, but they have a significant bias and therefore suffer from large drifts and require method for calibration like map matching. The system requires very little fixed infrastructure, the monetary cost is proportional to the number of users, rather than to the coverage area as is the case for traditional absolute indoor location systems.Siirretty Doriast

Drone heading calculation indoors

Abstract. Aim of this master’s thesis was to study drone flying indoors and propose a drone-implemented system that enables the drone heading calculation. In the outdoors, the heading is calculated effectively with a drone’s sensors but using them indoors is limited. Indoor positioning currently has not both low-cost and reliable solution for drone heading calculating. The differences between indoor flying principles and outdoor flying principles of the drone are described in the beginning of the thesis. Then different ways to determine the drone’s heading indoors and how they compare with one another are discussed. Finally, two different heading calculation methods are implemented and tested. The methods are based on using multiple location measurements on the drone and using machine vision together with machine learning. Both methods are affordable and are evaluated to see if they could enable drone flying indoors. First method gives out potential results based on testing results, but it needs further development to be able to always provide reliable heading. Second method shows poor results based on verification.Dronen lentosuunnan laskenta sisätiloissa. Tiivistelmä. Työn tavoitteena oli tutkia dronen lentämistä sisätiloissa ja ehdottaa sitä varten droneen implementoitavaa systeemiä, joka mahdollistaa dronen suunnan laskennan. Ulkona suuntatieto saadaan dronen sensorien avulla, mutta sisätiloissa niiden tarkkuus ei riitä samalla tavalla. Sisätilapaikannuksessa ei ole olemassa sekä edullista että luotettavaa ratkaisua dronen suunnan laskentaan. Työssä perehdytään aluksi dronen lentämisen periaatteisiin sisätiloissa ja miten ne eroavat ulkona lentämisestä. Sitten kerrotaan erilaisista keinoista määrittää dronen suunta sisätiloissa ja niiden keskinäisestä vertailusta. Lopuksi testataan kahta erilaista suunnan-laskenta-menetelmää, jotka perustuvat paikkatiedon käyttöön ja konenäköön yhdessä koneoppimisen kanssa. Menetelmät ovat edullisia ja niiden sopivuutta dronen sisälennätykseen arvioidaan. Ensimmäinen menetelmä antaa hyviä testituloksia mutta tarvitsee lisää jatkokehitystä, jotta se voisi antaa aina luotettavaa suuntatietoa. Toinen menetelmä antaa heikkoja tuloksia verifioinnin perusteella

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods