Non-Linear Multimodal Object Tracking Based on 2d Lidar Data

The contribution introduces a novel approach for tracking objects based on two-dimensional lidar data. As a central tracking engine, we employ a particle-filter-based solution which is capable of modelling non-linear dynamic processes as well as non-Gaussian noise distributions for the underlying process and sensor as well. In contrast to other lidar-based tracking approaches, no newly detected objects have to be associated to already known objects in an explicit manner. Since our weighting function is multi-modal, the association is done by the filter itself.