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.

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.

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 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.

This paper presents a feature extraction method in scanning laser range data. Many authors have studied this problem by proposing solutions that rely on a modeling of the scene in Cartesian coordinates. These methods are based on the computation of the interscan distance between two consecutive measurements, which, in practice, is not very easy to estimate. Our proposed method, i.e., segmentation using invariant parameters (SIP), deals with laser measurements in natural coordinates, which avoids any preprocessing stage that could modify the measurement noise statistics. This approach is founded on the use of an invariant description of the feature and leads to the definition of a criterion of line-segment detection that only depends on the sensor intrinsic parameters.