Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

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

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

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Adaptive Cardinal Heading Aided for Low Cost Foot-Mounted Inertial Pedestrian Navigation

The use of a low-cost MEMS-based Inertial Measurement Unit (IMU) provides a cost-effective approach for navigation purposes. Foot-mounted IMU is a popular option for indoor inertial pedestrian navigation, as a small and light MEMS-based inertial sensor can be tied to a pedestrian's foot or shoe. Without relying on GNSS or other external sensors to enhance navigation, the foot-mounted pedestrian navigation system can autonomously navigate, relying solely on the IMU. This is typically performed with the standard strapdown navigation algorithm in a Kalman filter, where Zero Velocity Updates (ZVU) are used together to restrict the error growth of the low-cost inertial sensors. ZVU is applied every time the user takes a step since there exists a zero velocity condition during stance phase. While velocity and correlated attitude errors can be estimated correctly using ZVUs, heading error is not because it is unobservable. In this paper, we extend our previous work to correct the heading error by aiding it using Multiple Polygon Areas (MPA) with adaptive weighting factor. We termed the approach as Adaptive Cardinal Heading Aided Inertial Navigation (A-CHAIN). We formulated an adaptive weighting factor applied to measurement noise to enhance measurement confidence. We then incorporated MPA heading into the algorithm, whereas multiple buildings with the same orientation are grouped together and assigned a specific heading information as a priori. Results shown that against the original CHAIN, the proposed Adaptive-CHAIN improved the position accuracy by more than five-fold

Wearable-Based pedestrian localization through fusjon of inertial sensor measurements

Hoy en día existe una gran demanda de sistemas de navegación personales integrados en servicios como gestión de desastres para personal de rescate. También se demandan sistemas de navegación personales como guía en grandes superficies, por ejemplo, hospitales, aeropuertos o centros comerciales. En esta tesis doctoral los escenarios estudiados son interiores y urbanos. La navegación se realiza por medio de sensores inerciales y magnéticos, idóneos por su amplia difusión, tamaño y peso reducido y porque no necesitan infraestructura. Se llevarán a cabo investigaciones para mejorar los algoritmos de navegación ya existentes y cubrir determinados aspectos aún no resueltos. En primer lugar se ha llevado a cabo un extenso análisis sobre los beneficios de usar medidas magnéticas para compensar los errores sistemáticos de los sensores inerciales, así como su efecto en la estimación de la orientación. Para ello se han usado medidas de referencia con valores de error conocidos combinando diferentes distribuciones de campos magnéticos. Los resultados obtenidos quedan respaldados con medidas realizadas con sensores reales de medio coste. Se ha concluido que el uso de medidas magnéticas es beneficioso porque acota errores en la orientación. Sin embargo, los escenarios bajo estudio suelen presentar campos magnéticos perturbados, lo que provoca que el proceso de estimación de errores sea prohibitivamente largo. En esta tesis doctoral se proponen algoritmos alternativos para el cálculo del desplazamiento horizontal del usuario, que han sido comparados con respecto a los ya existentes, ofreciendo los propuestos un mejor rendimiento. Además se incluye un innovador algoritmo para calcular el desplazamiento vertical del usuario, haciendo por primera vez posible obtener trayectorias en 3D usando solamente sensores inerciales no colocados en el zapato. Por último se propone un novedoso algoritmo capaz de prevenir errores de posición provocados por errores de rumbo. El algoritmo está basado en puntos de referencia automáticamente detectados por medio de medidas inerciales. Los puntos de referencia elegidos para los escenarios cubiertos son escaleras y esquinas, que al revisitarse permiten calcular el error acumulado en la trayectoria. Este error es compensado consiguiendo así acotar el error de rumbo. Este algoritmo ha sido extensamente probado con medidas de referencia y medidas realizadas con sensores reales de medio coste. La compensación de este error se adapta a las características del sistema de navegación personal

Filtering and Tracking for Pedestrian Dead-Reckoning System.

This thesis proposes a leader-follower system in which a robot, equipped with relatively sophisticated sensors, tracks and follows a human whose equipped with a low-fidelity odometry sensor called a Pedestrian Dead-Reckoning (PDR) device. Such a system is useful for "pack mule" applications, where the robot carries heavy loads for the humans. The proposed system is not dependent upon GPS, which can be jammed or obstructed. This human-following capability is made possible due to several novel contributions. First, we perform an in-depth analysis of our Pedestrian Dead-Reckoning (PDR) system with the Unscented Kalman Filter (UKF) and models of varying complexity. We propose an extension that limits elevation errors, and show that our proposed method reduces errors by 63% compared to a baseline method. We also propose a method for integrating magnetometers into the PDR framework, which automatically and opportunistically calibrates for hard/soft-iron effects and sensor misalignments. In a series of large-scale experiments, we show that this system achieves positional errors of less than 1.9% of the distance traveled. Finally, we propose methods that allow a robot to use LIDAR data to improve the accuracy of the robot's estimate of the human’s trajectory. These methods include: 1) a particle filter method and 2) two multi-hypothesis maximum-likelihood approaches based on stochastic gradient descent optimization. We show that the proposed approaches are able to track human trajectories in several synthetic and real-world datasets.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113500/1/suratkw_1.pd

IMag:Accurate and Rapidly Deployable Inertial Magneto-Inductive Localisation

Localisation is of importance for many applications. Our motivating scenarios are short-term construction work and emergency rescue. Not only is accuracy necessary, these scenarios also require rapid setup and robustness to environmental conditions. These requirements preclude the use of many traditional methods e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To solve these challenges, we introduce iMag, an accurate and rapidly deployable inertial magneto-inductive (MI) localisation system. It localises monitored workers using a single MI transmitter and inertial measurement units with minimal setup effort. However, MI location estimates can be distorted and ambiguous. To solve this problem, we suggest a novel method to use MI devices for sensing environmental distortions, and use these to correctly close inertial loops. By applying robust simultaneous localisation and mapping (SLAM), our proposed localisation method achieves excellent tracking accuracy, and can improve performance significantly compared with only using an inertial measurement unit (IMU) and MI device for localisation