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Publication# Indoor Navigation Performance Analysis

Résumé

INDOOR NAVIGATION PERFORMANCE ANALYSIS 1. Context of indoor navigation The objective of this research is to define and to implement a dedicated data model for indoor applications. Based on this specific model, route guidance and navigation algorithms can be integrated in order to develop applications with particular requirements. 2. Geodata modelling and algorithms for navigation In this paper, we present the development and the implementation of algorithms to access map databases by a user equipped with a pedestrian navigation system. The first step consists in building a 3D topological model especially designed for the localisation process. Rooms, corridors, stairs and halls must be assembled in order to provide a model for route guidance. The second step consists in the development of map-matching routines. The pedestrian navigation system provides the user’s position, which is combined with a link/node model. Developing a set of map matching functions for pedestrian navigation is a challenge because the style of human displacements indoors is fairly irregular. The final objective of the navigation system is to develop an integrated system which provides support when defining the travel, as well as guidance to the selected destination. For indoor navigation, the concept of “route” guidance must be reconsidered for several reasons: • The design of the map database is very different and the link/node model must be specific • The environment requires other means for localisation, which can accommodate rapid changes in the trajectory (up, down, left, right, backward). 3. Expected results The paper describes the following topics: • Design of the database developed for indoor applications • Implementation of short-path algorithms for route guidance • Evaluation of map-matching algorithms • Performance analysis of the integrated system • System test for several scenarii based on the use of a pedestrian navigation system

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Publications associées (11)

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Concepts associés (11)

Navigation

thumb|Porter un point ou tracer une route sur une carte marine à la passerelle de la frégate La Motte-Picquet.
La navigation est la science et l'ensemble des techniques qui permettent de :

- connaître

Papier

Le papier est un matériau en feuilles minces fabriqué à partir de fibres végétales. C'est un support d'écriture et de dessin avec de nombreuses autres applications. On appelle carton un papier épais

Récepteur GPS

Le système de navigation et de positionnement par satellite capte et analyse les signaux émis par une constellation de satellites. Les systèmes les plus connus sont GPS, GLONASS, Galileo et Beidu. M

Pierre-Yves Gilliéron, Bertrand Merminod

- Context of indoor navigation The objective of this research is to define and to implement a dedicated data model for indoor applications. Based on this specific model, route guidance and navigation algorithms can be integrated in order to develop applications with particular requirements. 2. Geodata modelling and algorithms for navigation In this paper, we present the development and the implementation of algorithms to access map databases by a user equipped with a pedestrian navigation system. The first step consists in building a 3D topological model especially designed for the localisation process. Rooms, corridors, stairs and halls must be assembled in order to provide a model for route guidance. The second step consists in the development of map-matching routines. The pedestrian navigation system provides the user’s position, which is combined with a link/node model. Developing a set of map matching functions for pedestrian navigation is a challenge because the style of human displacements indoors is fairly irregular. The final objective of the navigation system is to develop an integrated system which provides support when defining the travel, as well as guidance to the selected destination. For indoor navigation, the concept of “route” guidance must be reconsidered for several reasons: • The design of the map database is very different and the link/node model must be specific • The environment requires other means for localisation, which can accommodate rapid changes in the trajectory (up, down, left, right, backward). 3. Expected results The paper describes the following topics: • Design of the database developed for indoor applications • Implementation of short-path algorithms for route guidance • Evaluation of map-matching algorithms • Performance analysis of the integrated system • System test for several scenarii based on the use of a pedestrian navigation system

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.

2006Michel 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 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