Map updating in dynamic environments

While building maps when robot poses are known is a tractable problem requiring limited computational complexity, the simultaneous estimation of the trajectory and the map of the environment (known as SLAM) is much more complex and requires many computational resources. Moreover, SLAM is generally peformed in environments that do not vary over time (called static environments), whereas real applications commonly require navigation services in changing environments (called dynamic environments). Many real robotic applications require updated maps of the environment that vary over time, starting from a given known initial condition. In this context classical SLAM approaches are generally not directly applicable: such approaches only apply in static environments or in dynamic environments where it is possible to model the environment dynamics. We are interested here in long-term mapping operativity in presence of variations in the map, as in the case of robotic applications in logistic spaces, where rovers have to track the presence of goods in given areas. In this paper we propose a methodology that is able to detect variations in the environment, generate a local map containing only the persistent variations and finally merge the local map with the global one used for localization.