Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

The main problem of pedestrian dead-reckoning (PDR) using only a body-attached inertial measurement unit is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called heuristic drift elimination (HDE) that minimises the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of the four possible directions. In this article we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in the case of false matches with the building's dominant directions. Although magnetic compassing indoors has a chaotic behaviour, in this article we analyse large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this article also proposes an improved HDE method called Magnetically-aided Improved Heuristic Drift Elimination (MiHDE), that is implemented over a PDR framework that uses foot-mounted inertial navigation with an extended Kalman filter (EKF). The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories (e.g. curved paths) and also makes it robust against potential false dominant direction matchings

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

A Drift Eliminated Attitude & Position Estimation Algorithm In 3D

Inertial wearable sensors constitute a booming industry. They are self contained, low powered and highly miniaturized. They allow for remote or self monitoring of health-related parameters. When used to obtain 3-D position, velocity and orientation information, research has shown that it is possible to draw conclusion about issues such as fall risk, Parkinson disease and gait assessment. A key issues in extracting information from accelerometers and gyroscopes is the fusion of their noisy data to allow accurate assessment of the disease. This, so far, is an unsolved problem. Typically, a Kalman filter or its nonlinear, non-Gaussian version are implemented for estimating attitude â?? which in turn is critical for position estimation. However, sampling rates and large state vectors required make them unacceptable for the limited-capacity batteries of low-cost wearable sensors. The low-computation cost complementary filter has recently been re-emerging as the algorithm for attitude estimation. We employ it with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate. Inertial sensor data is obtained from the 10-axis SP-10C sensor, attached to a wearable insole that is inserted in the shoe. Data is obtained from walking in a structured indoor environment in Votey Hall

Increased error observability of an inertial pedestrian navigation system by rotating IMU

Indoor pedestrian navigation suffers from the unavailability of useful GNSS signals for navigation. Often a low-cost non-GNSS inertial sensor is used to navigate indoors. However, using only a low-cost inertial sensor for the system degrades its performance due to the low observability of errors affecting such low-cost sensors. Of particular concern is the heading drift error, caused primarily by the unobservability of z-axis gyro bias errors, which results in a huge positioning error when navigating for more than a few seconds. In this paper, the observability of this error is increased by proposing a method of rotating the inertial sensor on its y-axis. The results from a field trial for the proposed innovative method are presented. The method was performed by rotating the sensor mechanically–mounted on a shoe–on a single axis. The method was shown to increase the observability of z-axis gyro bias errors of a low-cost sensor. This is very significant because no other integrated measurements from other sensors are required to increase error observability. This should potentially be very useful for autonomous low-cost inertial pedestrian navigation systems that require a long period of navigation time

Localización de personas mediante sensores inerciales y su fusión con otras tecnologías

En el presente trabajo de Tesis se aborda el problema de la localización en entornos interiores utilizando sensores inerciales y su fusión con otras medidas para mejorar la estimación y limitar posibles derivas. Para ello, el algoritmo de localización propuesto se divide en tres partes: Una etapa de estimación del movimiento usando Pedestrian Dead Reckoning (PDR), un esquema de fusión de información que permite integrar múltiples tipos de medidas, aunque tengan relaciones no lineales, y la utilización de medidas externas (como la potencia de la señal de puntos de acceso WiFi, rangos a balizas UWB, GNSS, etc.) para limitar la deriva, proponiendo mejoras a cada una de ellas. Para mejorar el algoritmo PDR se propone la modificación del detector de apoyo utilizando un filtro de media sobre una ventana retardada. Para la estimación y corrección de errores se propone la utilización del filtro de Kalman Unscented (UKF) que simplifica los cálculos necesarios para la estimación y mejora la aproximación no lineal. Debido a la falta de información de la guiñada, una estimación PDR pura divergirá con el tiempo. Para aportar información de la orientación a la estimación se propone medir la rotación del campo magnético de acuerdo a las velocidades angulares observadas en el giróscopo. Se comprueba en varios experimentos que las mejoras evitan errores en la fase de apoyo, mejoran la estimación y disminuyen el efecto de la deriva de la orientación. Para fusionar la información del PDR con medidas externas se propone la utilización de dos esquemas: el primero, un filtro de límites que establece una distancia máxima entre 2 estimaciones, y el segundo un esquema basado en un filtro de partículas a dos etapas. El filtro de límites modifica la pdf (función de densidad de probabilidad) para evitar estimaciones muy distantes entre sí. Se comprueba que, al utilizar este método, se logra evitar la deriva un sistema PDR utilizando medidas UWB en otra parte del cuerpo. El esquema basado en un filtro de partículas utiliza la información de PDR para propagar las partículas y las medidas externas para actualizar los pesos de éstas. Se propone agregar el bias de la velocidad angular a los estados de las partículas para modelar el efecto del bias random walk (sesgo de camino aleatorio) del giróscopo. El filtro de partículas permite utilizar cualquier medida con una función de observación y una distribución de error, por lo que se estudian varios casos de estimaciones PDR fusionadas con medidas de sistemas WiFi, RFID, UWB y ZigBee. Los sistemas RF utilizados tienen un error de posicionamiento de 5 m (90 % de los casos) y la estimación PDR tiene un error creciente, pero al fusionar las estimaciones se logra un error inferior a 2 m (90 % de los casos). Por último, se utiliza el mapa del edificio para corregir las estimaciones y encauzarlas en las áreas caminables del edificio. Para ello se utiliza un método de eliminación de hipótesis (partículas) que atraviesan paredes. Este algoritmo se optimiza utilizando solo las paredes de la habitación en que se encuentra la partícula y se propone una sectorización de las operaciones para poder ser utilizada en MATLAB a tiempo real. Se demostró con señales reales que el algoritmo es capaz de auto localizar a una persona si el recorrido es no simétrico, obteniendo un nivel de error que dependerá del edificio, en nuestro caso cercano a 1 m. Si se utilizan medidas RF y el mapa, la estimación converge significativamente más rápido, y el nivel de error y el número de partículas necesarias (por ende, el tiempo de cómputo) disminuyen

Erfassung von Innenraummodellen mittels Smartphones

Unter Verwendung von aufgezeichneten Bewegungsspuren eines Inertialsensors ist es möglich, ein Innenraummodell eines Gebäudes zu generieren. Das Innenraummodell unterscheidet hierbei Räume und Korridore. Die Bewegungsspuren werden durch auf dem Fuß platzierte Inertialsensoren per ZUPT erfasst. Es wird von einer Vielzahl an Benutzern ausgegangen, welche sich in alltäglichen Situationen durch Gebäude bewegen und mit den Sensoren ihres Smartphones opportunistisch Daten erfassen. Bewegungsspuren werden in gerade Segmente unterteilt. Durch eine Äquivalenzrelation wird festgestellt, ob sich der Benutzer beim Erfassen der Spuren auf demselben Korridor befunden hatte. Die Geometrie von Korridoren wird durch Quantile und die empirische Verteilungsfunktion bestimmt. Durch die Ausrichtung der Spuren anhand der Geometrie der Korridore, können überstehende Abschnitte durch geeignete Kriterien als Räume erkannt werden. Für die Evaluation wurden von vier Testpersonen über 200 Spuren in alltäglichen Szenarien aufgenommen. Wählt man aus diesen Spuren 90 aus, so werden im Durchschnitt über 90% aller Korridore des Stockwerks erkannt. In 65% der so generierten Innenraummodelle war die durchschnittliche Verschiebung der Korridore kleiner als 1,5m.It is possible to generate indoor models by using traces recorded by inertial measurement units. The generated indoor model distinguishes between rooms and corridors. Traces will be collected by foot-mounted inertial measurement units via ZUPT. The data will be collected in a crowd based approach via Smartphones and sensor units carried by users. Users will walk inside the building in all-day situations, collecting data opportunistically. The collected traces will be segmented into parts where the user walked straight. Using a equivalence relation, segments collected from the same corridor can be combined. Reconstructing the geometry of corridors will use quantiles and the empirical distribution function. Using a method to correct traces via the constructed corridor geometry, rooms can be found by protruding parts of traces. To evaluate the system, four volunteers collected over 200 traces in everyday scenarios. Choosing 90 out of them, in average 90% of all corridors will be found. In 65% of this constructed indoor models, the average shift of corridors was less than 1.5 m