Map database management

Map database management systems are software programs designed to store and recall spatial information for navigation applications, and are thus a form of Geographic information system. They are widely used in localization and navigation, especially in automotive applications. Moreover, they are playing an increasingly important role in the emerging areas of location-based services, active safety functions and advanced driver-assistance systems. Common to these functions is the requirement for an on-board map database that contains information describing the road network. When designed well, a map database enables the rapid indexing and lookup of a large amount of geographic data. Data model (GIS) Maps are stored as graphs, or two dimensional arrays of objects with attributes of location and category, where some common categories include parks, roads, cities, and the like. A map database represents a road network along with associated features. Map providers can choose various models of a road network as a basis to formulate a database. Commonly, such a model comprises basic elements (nodes, links and areas) of the road network and properties of those elements (location coordinates, shape, addresses, road class, speed range, etc.). The basic elements are referred to as features and the properties as attributes. Other information associated with the road network is also included, including points of interest, building shapes, and political boundaries. This is shown schematically in the adjacent image. (GDF) is a standardized description of such a model. Each node within a map graph represents a point location of the surface of the Earth and is represented by a pair of longitude (lon) and latitude (lat) coordinates. Each link represents a stretch of road between two nodes, and is represented by a line segment (corresponding to a straight section of road) or a curve having a shape that is generally described by intermediate points (called shape points) along the link.

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Map database management

Traffic message channel

Traffic Message Channel (TMC) is a technology for delivering traffic and travel information to motor vehicle drivers. It is digitally coded using the ALERT C or TPEG protocol into Radio Data System (RDS) carried via conventional FM radio broadcasts. It can also be transmitted on Digital Audio Broadcasting or satellite radio. TMC allows silent delivery of dynamic information suitable for reproduction or display in the user's language without interrupting audio broadcast services.

Intelligent transportation system

An intelligent transportation system (ITS) is an advanced application which aims to provide innovative services relating to different modes of transport and traffic management and enable users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. Some of these technologies include calling for emergency services when an accident occurs, using cameras to enforce traffic laws or signs that mark speed limit changes depending on conditions.

Algorithms for map-aided autonomous indoor pedestrian positioning and navigation

The personal positioning and navigation became a very challenging topic in our dynamic time. The urban canyons and particularly indoors represent the most difficult areas for personal navigation problematic. Problems like disturbed satellite signals make the positioning impossible indoors. Recently developed systems for indoor positioning do not assure the necessary positioning accuracy or are very expensive. Our concept stands for a fully autonomous positioning and navigation process. That is, a method that does not rely on the reception of external information, like satellite or terrestrial signals. Therefore, this research is based on the use of inertial measurements of the human walk and the map database which contains the graphic representation of the elements of the building, created by applying the link-node model. Using this reduced set of information the task is to develop methodology, based on the interaction of the data from both sources, to assure reliable positioning and navigation process. This research is divided in three parts. The first part consists in the development of a methodology for initial localization of the person indoors. The problem to solve is to localize the person in the building. Consider a person equipped with a system which contains set of inertial sensors and map database of the building. Speed, turn rate and barometric altitude are measured and time-stamped on each step of the person. A pre-processing phase uses these raw measurements in order to construct a polyline, thus representing user's trajectory. In the localization approach central place takes the association of the user's trajectory with the graph representation of the building, process known as map-matching. The solution is based on statistical method where the determination of the user's position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Initial localization determines the edge of the graph occupied by the person. The second part aims at continuous localization, where user's position is estimated on every step. Besides the application of the classical map-matching techniques, two new methods are developed. Both rely on the similarity of the geometry of the trajectory and the elements of the graph. The first is based on the Bayesian inference, where the estimation is computed considering the walked distance and azimuth. The second method represents a new application of the Fréchet distance as degree of similarity between two polylines. The third part is pointed at the pedestrian guidance. Once the user's position is known it is easy to compute the path to his destination and to give him directions. The problem is to assure continuance of the process of navigation in the case when the person has lost his path. In that case the solution consists in either giving instructions to the user to go back on the path or computation of a new path from the actual position of the user to his destination. Based on that methodology, algorithms for initial localization, continuous localization, and guidance were created. Numerous tests with the participation of several persons have been provided in order to validate the algorithms and to show their performance, robustness and limits.

EPFL2007

Map-based Autonomous Personal Localization and Tracking

Michel Bierlaire, Bertrand Merminod

An autonomous localisation and tracking method is a method independent from the reception of external data. In our approach we ignore methods like GPS- and WiFi- positioning and we focus on the use of inertial navigation system (INS) carried by the person and connected to a map database. The walking person is considered as a dynamic system, whose movements are measured by the INS. His trajectory is modified with respect to a dedicated motion model. Users location is estimated in the frame of Bayesian inference and is based on the association of the trajectory to the map database, a technique known as map- matching. Because of the non-linear nature of the estimation problem, non-inear filtering techniques like particle filters (Sequential Monte Carlo methods) are applied. In tracking mode simple geometric constrains will be observed in order to associate every point of the trajectory to element of the map database. In parallel the localisation mode rest active in order to keep the knowledge on the history of measurements. The algorithm is tested on the campus of EPFL; the process of localisation is entirely autonomous and gives promising results. That method of localisation can be applied to many pedestrian navigation tasks, in particular for the needs of the fire-brigades and security services.

2007

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

Elements of Geomatics

Ce cours de base en géomatique présente les concepts et méthodes d’acquisition, de gestion et de représentation des géodonnées. Il inclut les bases de topométrie, géodésie et cartographie, avec un acc

Elements of Geomatics