Capteurs et algorithmes pour la localisation autonome en mode pédestre

The challenge of knowing one's position in a precise and reliable way, at any time, with and without reception of satellite signals, represents an area fairly explored for the navigation of vehicles. To widen this service to the pedestrians requires a different approach that adapts to the dynamics, to the speed and especially to the total freedom of movement of the people. The traditional approach implements a triad of accelerometers and gyroscopes, which signals are integrated to obtain the relative displacement. This concept is unfortunately not judicious for a low-cost system. The principal reason is that the speed of displacement of a person is lost in the sensor noise level. In order to take into account all these specificities, an occurential approach was developed, based upon a subset of sensors as well as physiological and biomechanical parameters of the walk. This research is divided into three main directions. The first area of interest consists in the determination of the physiological parameters necessary to quantify the speed of walk and the step length. While the agitation of the accelerometer signals is a good speedometer, the frequency of the steps improves the robustness of the models. The influence of the gender added to the great human diversity imply the normalisation of the various relations deduced. Many tests carried out under conditions of everyday life reveal that the variation of the stride length, especially with the slope, strongly depends on the physical training of the person as well as on the duration of the climb or descent. Characteristic pattern were identified to differentiate between the forward, backward and lateral movements. The various suggested models were then favourably tested with some blind people, whose walking rhythm strongly varies according to the degree of confidence they have towards the course. The second part directly relates to the multiple technologies integrated to build an autonomous three-dimensional Pedestrian Navigation Module (PNM). The knowledge of the terrestrial magnetic field and its orientation makes it possible to determine the azimuth of displacement of a person. The use of a gyroscope improves the reliability of the system and facilitates the detection of magnetic disturbances. More stable in the short term than the compass, it is therefore the optimal complement under such circumstances. The altimetric information is obtained by barometric measurements which, according to the required precision, can be differential. The implementation of a GPS receiver allows the absolute positioning simultaneously to the calibration of the different sensor parameters and physiological models. The third part describes the integration of the models and measurements as well as the characteristics and treatments specific to pedestrian navigation. An initialisation phase is presented to individualize the parameters of the walk and adapt them from the general model. Hence, thanks to the compass-gyroscope integration together with the detection of any movement, this allows an optimal determination and filtering of the azimuth that has little or no temporal degradation. The consideration of several phenomena specific to the displacements of the humans brings artificial intelligence in pedestrian navigation. The coupling of the various sources of measurements, the influence of their precision on the computed position as well as their implication on the PNM reliability are described and illustrated. More than 550 km covered in various circumstances by 31 people allowed to validate the presented approach while fixing its limits.

GPS/INS integration for pedestrian navigation

This research has been sponsored by the Centre Suisse d'Electronique et de Microtechnique (CSEM) in Neuchâtel, Switzerland. It introduces a system and the algorithms for Pedestrian Navigation using a combination of sensors. The main objective is to localise a pedestrian anywhere and at any moment. The equipments utilised to fulfill this requirement are a Global Navigation Satellite System (GNSS) receiver and inertial sensors, which are attached to the person and as such need to be portable. An overview of Pedestrian Navigation constitutes the first part of the document. This new domain is examined from four different views: applications, tools (or sensors), architecture of the system and finally environment in which the pedestrian is travelling. As part of this process, the "state of the art" situation is presented. The approach followed in order to aid pedestrian to navigate is based on the Dead Reckoning technique coupled with GNSS. Consequently, the resolution of the travelled "distance" is separated from the resolution of the orientation of the walk. For the computation of the distance, a new technique based upon accelerometers and GNSS has been developed and demonstrated. The accelerometers are not used as a classical pedometer (counter of the number of steps) and the technique is not based on the double integration to obtain successively speed and distance. Instead, signal processing allows, considering individual parameters, the walking speed to be obtained directly from the signal of the accelerometers. This process, as well as the manner to determine the individual parameters, is presented in detail. The development of the algorithms is based on research performed in both the navigation and the medical domains (mainly in physiology). The computation of the orientation is more classical. It is based on the measurements made by a gyroscope and a GNSS receiver. The particularity of this study is the use of a single gyroscope to determine the orientation of the walk instead of three for the classical technique of inertial navigation. The influence of body movement on the gyroscope output has been deeply examined to determine the most appropriate way to process the signal of the gyroscope. The feasibility of the use of a single gyro, in the context of pedestrian navigation, is demonstrated. The potential added value for introducing a magnetic compass in the system is also evaluated. Finally a centralised Kalman filter has been designed and tested to merge all the sensors outputs, to combine the distance and the orientation, to integrate the Dead Reckoning solution and the GNSS solutions and to estimate all the parameters in a close to real-time process. The efficiency of this filter is demonstrated through different tests.

EPFL2003

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

Quentin Ladetto, Bertrand Merminod

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.

2002

Bayesian Approach for Indoor Pedestrian Localisation

Michel Bierlaire, Bertrand Merminod

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 GPS receiver and 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.

2006

GPS/INS integration for pedestrian navigation

EPFL2003