An Alternative Approach to Vision Techniques - Pedestrian Navigation System based on Digital Magnetic Compass and Gyroscope Integration

Over the last few years, research had been conducted on how to develop basic mobility aid for visually impaired and blind people using vision and image processing techniques. However, our research at Geodetic Engineering Laboratory has taken a different view to this problem

Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning

Activity monitors based on accelerometry are used to predict the speed and energy cost of walking at 0% slope, but not at other inclinations. Parallel measurements of body accelerations and altitude variation were studied to determine whether walking speed prediction could be improved. Fourteen subjects walked twice along a 1.3km circuit with substantial slope variations (−17% to +17%). The parameters recorded were body acceleration using a uni-axial accelerometer, altitude variation using differential barometry, and walking speed using satellite positioning (DGPS). Linear regressions were calculated between acceleration and walking speed, and between acceleration/altitude and walking speed. These predictive models, calculated using the data from the first circuit run, were used to predict speed during the second circuit. Finally the predicted velocity was compared with the measured one. The result was that acceleration alone failed to predict speed (meanr=0.4). Adding altitude variation improved the prediction (meanr=0.7). With regard to the altitude/acceleration-speed relationship, substantial inter-individual variation was found. It is concluded that accelerometry, combined with altitude measurement, can assess position variations of humans provided inter-individual variation is taken into account. It is also confirmed that DGPS can be used for outdoor walking speed measurements, opening up new perspectives in the field of biomechanic

In Step with INS Navigation for the Blind, Tracking Emergency Crews

As demand increases for positioning rescue crews, military users, and individuals with special needs, miniaturized low-power inertial measurement units (IMUs) coupled with GPS receivers and other sensors can provide accurate position in both indoor and outdoor situations

Système temps réel de lever précis de la géométrie des axes routiers

Le développement de la télématique des transports routiers réclame des données géographiques de premier ordre. Les systèmes de mobile mapping peuvent acquérir ces informations en offrant une productivité incomparable grâce à la combinaison de capteurs de localisation et de vidéogrammétrie. Le laboratoire de Topométrie de l’EPFL a développé le système Photobus qui permet de lever la géométrie des axes routiers avec une très haute qualité. Cet article présente l’architecture et les composants principaux de cette plateforme de mobile mapping : un instrument de localisation de grande précision basé sur la localisation par satellites et un système de caméra numérique basé sur la technologie CMOS. L’article présente quelques résultats issus d’une campagne de mesures, dont la précision planimétrique est de l’ordre de 10 cm

Bayesian Inference for Autonomous Personal Localisation Indoors

The principal concept of navigation is to start from a known (initial) position and to ensure a continued and reliable localisation of the user during his/her movement. The initial position of the trajectory is usually obtained via GPS or defined by the user. Consider a pedestrian navigation system which contains a set of inertial sensors, connected with a map database. In the urban environment and indoors the localisation depends entirely on the measurements from the inertial sensors. The trajectory is defined in a local coordinate system and with an arbitrary orientation. The problem to solve is to determine the users location using the map database and inertial measurements of the navigation system. The idea behind our approach is to find the location and orientation of the trajectory and thus the users location. The proposed solution associates the users trajectory with the map database applying statistical methods in combination with map-matching. Similar geometric forms must be identified in both the trajectory and the link-node model. The trajectory, defined by a set of consecutive points, is transformed to a set of lines thanks to a dedicated motion model. In this research we propose a solution based on statistical methods where the history of the route and actual measurements are treated at the same time. The determination of the absolute position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Following this approach the posterior estimation of the users location can be calculated using prior information and actual measurements. Because of the non-linear nature of the estimation problem, non-linear filtering techniques like particle filters (Sequential Monte Carlo methods) are applied

Map-matching for pedestrians via Bayesian inference

A navigation process is to start from a known (initial) position and to ensure a continued localisation of the user during the movement. Consider a pedestrian navigation system which contains a GPS receiver and a set of inertial sensors connected with the map database. The problem to solve is to determine the users location using the map database and measurements of the inertial sensors. Indoors the position of each step is determined as a function of the previous position and inertial measurements. Thus the trajectory is defined in a local coordinate system and with an arbitrary orientation. A dedicated motion model transforms the trajectory from set of consecutive points to a polygon. Then we must associate similar details from both data sources, the modified trajectory and the link-node model. The trajectory can be considered as the history of the route and its last point as the actual position of the user. In this research we propose a solution based on statistical methods and map-matching. The determination of the absolute position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Following this approach the posterior estimation of the users position can be calculated using prior information and actual measurements. Because of the non-linear nature of the estimation problem, non-linear filtering techniques like particle filters (sequential Monte Carlo methods) are applied

Faciliter le déplacement des aveugles avec une carte numérique et une interface vocale

Le récepteur GPS permet à la personne de se localiser sous certaines conditions de visibilité des satellites et de réception de leurs signaux. Lorsque ces conditions ne sont pas remplies, il est nécessaire d'utiliser d'autres capteurs pour saisir le déplacement de la personne et son orientation. Ce mécanisme est appelé navigation à l'estime (Dead Reckoning)

Capteurs et analyse de signaux pour la navigation pédestre

Cet article présente une approche pour améliorer la détermination de l'azimut d'un système de navigation pédestre. Le système étudié combine un gyroscope, un compas magnétique et un accéléromètre tri-axial

Digital Magnetic Compass and Gyroscope for Dismounted Soldier Position & Navigation

When satellite signals are available, the localisation of a pedestrian is fairly straightforward. However, in cities or indoors, dead reckoning systems are necessary