Christophe Boucher

A Hybrid Particle Approach for GNSS Applications With Partial GPS Outages

To provide an accurate positioning, the land vehicle navigation applications are based on global positioning system (GPS). The addition of a digital road map allows locating the vehicle continuously and helps the driver to get the best path. These systems are usually enhanced with dead reckoning sensors due to GPS outages in urban areas in particular. For instance, the odometer sensors can be used to correct the vehicle location in this case. We present here a global estimation method of solving the fusion problem of the GPS, odometer, and digital road map measurements in the presence of GPS outages. It relies on a hybrid filter that takes advantage of the combination of a Kalman filter, which computes the linear part of the state equations and a particle filter to provide an optimal resolution scheme. When GPS fails, the filter fuses all available pseudorange measures to improve the vehicle positioning. In the case of an urban transport scenario, the results show that the number of particles is significantly reduced to achieve the same performance of a single particle filter in terms of accuracy. Moreover, software solutions can be developed for real-time applications.

3D structure and motion recovery in a multisensor framework

Abstract The aim of this article is to develop a multisensor estimation method to identify the 3D structure and motion of an object. The method lies in the feature description of the object and the solution uses an extended Kalman filter (EKF) which fuses information from each sensor. The filter tracks the features through the data sequences and estimates the 3D position and affines motion parameters. The originality of this work relies on a 3D modelling of this problem to jointly estimate the 3D structure and motion. This estimation is made possible by the use of an active sensor (range camera).

Fusion of GPS, OSM and DEM Data for Estimating Road Network Elevation

This paper presents a method to estimate the roads elevation by fusing data from GPS receivers, OSM road network and DEM terrain surface. It relies on GPS data collected from a vehicle that travels the OSM road network. Also, a digital elevation model from SRTM data is combined in order to get a discrete elevation of the road. The fusion algorithm implements an unscented Kalman filter in a centralized scheme. Here, roadmaps and DEM data are modeled as measurement equations that allows to account for their errors and uncertainties. The method highlights the advantage of a probabilistic dual-matching, based on the computation of Mahalanobis distances, that allows to identify and match GPS positioning with the OSM road network and the DEM terrain surface. Experimental results show that the proposed method leads to improve the road elevation estimation with respect to conventional approaches using DEM data only.

Automatic Detection of Topological Changes for Digital Road Map Updating

This paper highlights a method to detect topological changes of digital road maps automatically. Here, the road network updating can be done in real time. It relies on collected GPS data from vehicles. These data are fused with existing road map data by an unscented Kalman filter in a centralized scheme. We modeled the map data as a sensor that allows accounting for the errors and uncertainties of land surveying. The core of our method is a probabilistic map-matching approach that is used to manage the road network database through the computation of the Mahalanobis distance. We show experimental results from an urban transport network scenario in which we deal with the case of opened roads and the recent conversion of a crossroad into a roundabout. Then, the obsolete map database is updated to ensure a more reliable route planning.

Multisensor unscented filtering for GPS-based navigation systems

The GPS-based navigation systems provide an accurate positioning of land-vehicles but this service is not always continuous in urban areas. These systems are usually enhanced with dead reckoning sensors, like odometers, and the use of a digital road map can improve the vehicle location when GPS fails. In this work, we focus on these GPS outages where the main problem is to combine another available measurements intelligently. We present here a global estimation method to solve the fusion problem of the GPS, odometer and digital road map measurements. It relies on an unscented Kalman filter whose main benefit is to deal with non-linear equations directly. In the case of an urban transport scenario, the comparison with an extended Kalman filter shows that the vehicle positioning is improved without requiring more computing time.

3D particle tracking using an active vision

This paper concerns the 3D motion estimation and tracking of an object in a scene using an active vision system. We develop a state model which describes in an unified way this estimation problem. In this case, the main difficulty lies in the non-linear state equations. They are solved by the particle filter which avoids the linearization stage.

A General Framework for 3-D Parameters Estimation of Roads Using GPS, OSM and DEM Data

A growing number of applications needs GIS mapping information and commercial 3-D roadmaps especially. This paper presents a solution of accessing freely to 3-D map information and updating in the context of transport applications. The method relies on the OSM road networks that is 2-D modeled intrinsically. The objective is to estimate the road elevation and inclination parameters by fusing GPS, OSM and DEM data through a nonlinear filter. An experimental framework, using ASTER GDEM2 data, shows some results of the improvement of the roads modeling that includes their slopes also. The map database can be enriched with the estimated inclinations. The accuracy depends on the GPS and DEM elevation errors (typically a few meters with the GNSS sensors used and the DEM under consideration).

Automatic estimation of road inclinations by fusing GPS readings with OSM and ASTER GDEM2 data

This work focuses on a method of estimating the slope of road networks that are ground-modeled by OSM originally. The aim is to get 3-D road vectors including their 2-D location and inclination, that is an important parameter to ensure more reliable route planning. This is done from GPS data that are collected by a vehicle traveling on an existing OSM road network whose a DEM, like SRTM or ASTER data, provides a modeling of the terrain surface. GPS, OSM and DEM data are modeled as measurement equations in order to account for their errors through an UKF that fuses them in a centralized scheme. Here, the key step is to match GPS/OSM/DEM measurements successively by computing statistical Mahalanobis distances. The experimental framework show some results of road inclinations estimation and the significant contribution of a DEM as baseline.

Dual-GPS fusion for automatic enhancement of digital OSM roadmaps

This paper presents a method to enhance digital OSM maps with roads inclination automatically. It relies on GPS data collected from two receivers. These data are fused with existing road map data by implementing the unscented Kalman filtering in a centralized scheme. We modeled the map data as a sensor that allows to account for its errors and uncertainties. The core of our method relies on a probabilistic map-matching approach that is used to manage the road network database through the computation of the Mahalanobis distance. We show experimental results from an extra-urban road network scenario in which the inclination of matched road segments is estimated to ensure a more reliable route planning.

3D structure and motion recovery by fusing range and intensity image sequences

Proposes a 3D dynamic image reconstruction method based on the fusion of data from sequences of range and intensity images. The vision system acquires both images of the scene at each time instance. We use a line segment description of the objects of the scene, and more precisely a point representation of the segment described by its extremities. The detailed solution to this estimation problem lies in a global filter that matches the segments through the range/intensity image sequences and fuses both data to recover the 3D structure and motion of an object. The method performs well on synthetic image sequences from two sensors and is now being applied to an experimental sequence of an object evolving according to a rotation/translation motion. The scene is viewed by a range camera which delivers a range image and an intensity (reflectance) image.