Stereo Vision for Dynamic Urban Environment Perception Using Semantic Context in Evidential Grid

Uncertainty from urban environment arises not only by the imprecise pose estimation and noisy information in images but also by the lack of semantic information. This paper presents an approach to improve the perception capability of intelligent vehicles in complex urban environments. The method uses the meta-knowledge acquired from a built Semantic Context image and applies it on evidential grids constructed from the Stereo Vision. In the detection of semantic information problem, Texton and Dispton maps are used as a source to model a probabilistic joint boosting classifier. The evidential grids are based on occupancy grids, an approach based on the Dempster-Shafer theory that manages different sources of uncertainty arising in dynamic temporal scenes. An additional structure is created to handle a refined set of propositions originated from the meta-knowledge available. This structure bound with evidential conflict analysis enables an accurate detection of stationary and mobile objects in the perception grid. The proposed work reports real experiments carried out in a challenging urban environment using the KITTI benchmark in which meaningful evaluation can be done to illustrate the validity and application of this approach.