Sequential Monte Carlo methods for tracking heterogeneous multiple objects in raw data of laser telemetry
This thesis focus on the problem of multiobject detection and tracking multiple moving objects on the road, using a scanning laser rangefinder. The works in the field of obstacle detection and tracking from lidar data generally use three main stages : detection, measurement association and filtering. However, it is known that this processing chain can lead to a loss of information that may be reponsible for non-detection or false alarm problems. Furthermore, the non-linearities associated to the polar-to-Cartesian transformation of lidar measurements during the detection step cannot preserve the statistical properties of the measurement noise. Another difficulty, related to the spatially distributed nature of a lidar measurements of an object, is to associate each impact with a single vehicle while taking into account the temporal variability of the number of impacts. An approach that only exploits the raw data ensures the optimality of the processing chain. This thesis explores a new joint approach for detection and tracking that uses raw lidar data, while eliminating any step of predetection. The proposed approach is based, first, on the use of sequential Monte Carlo methods due to their ability to deal with highly non-linear models, and secondly, on an object modeling related to lidar measure. The method is validated with data from the simulator SIVIC under different experimental conditions for the detection and tracking of heterogeneous objects with monolayer and multilayer lidar.