Visual navigation of communicating vehicles in unknown and changing environment

Abstract

This paper describes the design and testing of a system to enable large scale cooperative navigation of autonomous vehicles moving on a priori unknown routes in changing environments. A large-scale learning-mapping approach and a replay-localization method are combined to achieve cooperative navigation. The mapping approach is based on a proposed hierarchical/hybrid BiCam SLAM approach-global level and local maps-, which will be generalized to be executed on multiple vehicles moving as a convoy. A global 3D map maintains the relationships between a series of local submaps built by the first vehicle of the convoy (leader), defining a path that all other vehicles (followers) must stay on. Only single camera setups are considered. The overall approach is evaluated with real data acquired in an urban environment.