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International audience; Given the boom linked to smart mobility, transport systems require increasingly precise and relevant navigation applications to offer optimized journeys in terms of time and energy consumption, such as for HEV. Most of these navigation applications are based on the processing of 2D digital road maps, while taking into account the GNSS location of vehicles. These localization systems also integrate sensors such as accelerometers and gyroscopes to overcome the well-known problems of GPS positioning, even if the current limited introduction of IoT in the transport industry has made it possible to develop new aided-GPS methods such as geofencing. This paper focuses on one important parameter in the journey optimization of land vehicles: the road slope. We propose a method to estimate the roads’ inclination parameters by fusing GNSS, INS, OSM and ASTER GDEM data through a nonlinear filter. The incremental estimate of the slope will complement the 2D modeling of the roads already available in OpenStreetMap and could be used in route planning optimization. The scientific novelty lies more specifically in the statistical map-matching approaches that we develop both for OSM and DEM data. Estimation results of the roads slopes are shown in experimental conditions.

The objective of this work is to estimate the localization and attitude of a land-vehicle by fusing GPS, OSM and DEM data through a nonlinear filter. We focus on the heading and pitch angles of the vehicle, knowing that these parameters are essential in the optimization of the route planning and energy management for an EV. This paper investigates the performance of particle filtering and probabilistic map-matching algorithms for tracking a vehicle with the help of digital roadmaps to improve the ground-location. Also, the filter fuses DEM data through a TIN method in order to bound altitude errors caused by GPS. The proposed method is evaluated through an urban transport network scenario and experimental results show that the proposed estimator can accurately estimate the vehicle location and attitude.

This study concerns the implementation of an underwater positioning system in shallow water environment. Shallow water localization of the beacon is done by using the propagation of acoustic signals in water. The experiment is based on the use of a boat equipped with two hydrophones and an emitting beacon. The signal received on each sensors are processed to compute the difference in time arrival between them (TDOA) and to derive the boat location. In this paper a close loop architecture is proposed to process the signal. This architecture based on a Kalman filter allows to track the peaks of correlation in a delay Doppler map. Underwater tests are done to validate the method. They lead to a precision of about one meter.

Classical GPS/Acoustic positioning techniques using ranging position location in low depth environment suffer from the bad system geometry. The aim of this article is to propose a new hyperbolic position location technique that takes into account the system geometry. In our approach we process the position with the normal distribution of the beacon location defined as a sum of uniform distributions. A uniform distribution is derived from the surface of possible beacon localizations obtained with approximations of the hyperbolic approach in the case of noisy observations and for two boat positions. These surfaces increase when the geometry of the system is degraded and the probability in these cases decreases. These lower probabilities allow to minimize the influence of the bad geometry configuration in the finale estimation process. The proposed method is assessed on synthetic data and compared to the classical ranging approach.

A method derived from the adaptive Sampling Importance Resampling particle filter is proposed to solve vehicle detection and tracking problems in laser range finder data. The originality of this approach lies in a joint detection and tracking of the objects. To this end, the solution is based on a matched filter which uses a predefined vehicle model. The non-linearity of the state equations is tackled by sequential Monte Carlo methods which are here the basis of our solution. A central point here is to calculate the weights of the matched particle filter, according to the vehicle model. The proposed approach is then applied to synthetic data from a road scenario. The efficiency of the method is shown in terms of estimation accuracies and detection.

This paper presents a method for joint detection and tracking of vehicles with a scanning laser rangefinder. The lidar measurements of an object have the particularity to be spatially distributed, which generally leads to a detection step before any tracking. Differently, the proposed method relies on the raw measurement processing without any detection step, which improves the overall performance in multiobject tracking while providing good estimation accuracies. The solution uses the sequential Monte Carlo methods by incorporating the geometric invariant of the objects of interest (vehicles). This approach also offers an efficient solution to the problem of multitarget tracking by integrating naturally the track management in the filtering process.

This article deals with an estimation method of 3D structure and motion. The object is described by line segments and points assuming that it can be described by a polygonal model. It models the problem which is solved by a nonlinear estimation method: The Particle Filter. This method can account for nonlinear models and non-Gaussian statistics without any linearization stage like the Extended Kalman Filter (EKF), for example. The increase in accuracy is shown on a vision system composed by sensors delivering range and intensity/reflectance image sequences. Finally, the solution is compared with a commonly used state estimation method (EKF).

In this paper, we present a method for detecting and tracking rigid moving objects in a monocular image sequence. The originality of this method lies in a state modelling of this estimation problem which is solved in an unified way. This hybrid estimation problem leads to non-linear state equations that are solved by the particle method. A particle filter is set for each shape model (modes). It estimates the motion and position parameters and tracks the object in the sequence. The algorithm also computes at each time the probability of all modes. This method is then applied to synthetic and real image sequences in order to evaluate the estimation accuracies and the robustness of the tracking procedure.

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.

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.

Linear discriminant analysis, such as Fisher's criterion, is a statistical learning tool traditionally devoted to separating a training dataset into two or even several classes by the way of linear decision boundaries. In this paper, we show that this tool can formalize the robust linear regression problem as a robust estimator will do. More precisely, we develop a one-class Fischer's criterion in which the maximization provides both the regression parameters and the separation of the data in two classes: typical data and atypical data or outliers. This new criterion is built on the statistical properties of the subspace decomposition of the hat matrix. From this angle, we improve the discriminative properties of the hat matrix which is traditionally used as outlier diagnostic measure in linear regression. Naturally, we call this new approach discriminative hat matrix. The proposed algorithm is fully nonsupervised and needs only the initialization of one parameter. Synthetic and real datasets are used to study the performance both in terms of regression and classification of the proposed approach. We also illustrate its potential application to image recognition and fundamental matrix estimation in computer vision.

This paper presents a detection and tracking approach of multiple vehicles in scanning laser range data. The proposed solution relies on a new detection method based on object geometric invariant that uses the raw measurements directly in polar coordinates. The multitarget management problem is solved in the PHD framework by a particle filter.

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.

This paper presents a method for joint detection and tracking of vehicles in scanning laser range data. Many methods use a solution that processes the raw data in a detection procedure and then tracks the detected object in an association/tracking procedure. The proposed approach uses a preclustering stage (SIP) as an input of the tracking process that allows to manage the displacement of the center-of-gravity and the changes in the apparent shape from object and motion modeling. The global problem is then described using a state-space modeling which is solved by a nonlinear filtering method.

This work highlights a method to manage road map databases in real-time. This method is able to detect missing roads and to update the databases from GPS data. It relies on an unscented Kalman filter whose main benefit is to deal with non-linear equations directly and to fuse available GPS and road map data in a centralized fusion scheme. We modeled the digital road map as an additional sensor which allows to account for the errors and uncertainties of the database. A probabilistic map-matching algorithm 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 where roads are missing due to an outdated map database.