Mapping in dynamic environments using stereo vision

Next generation driver assistance systems demand a precise perception of the vicinity of the vehicle. Sensor readings are usually harnessed to gain knowledge of all moving and stationary obstacles. Commonly two paradigms are followed. Stationary environments are well modeled by non-parametric occupancy grids. Contrary, moving objects require a tracking and are not well suited for grid-based techniques. However, tracking objects requires some prior knowledge and parameterization which is inferior for unstructured cluttered obstacles. Herein we augment a grid-based mapping method designed for static environments with object tracking hence complementing both approaches. To this end we classify regions of stereo images into moving and stationary parts. The stationary part is fused in our static grid whereas moving parts are tracked yielding reliable motion estimates. The classifier used to distinguish moving from stationary parts is based on a Sequential Probability Ratio Test (SPRT), a model selection method which blends well into the tracking architecture. Thereby we achieve real-time operability on modest computing hardware.