A novel approach to the SLAM problem has been tested in an industrial environment within a robotic assistance context. In order to be fully reliable in non-modelled circumstances where the environment cannot be considered as known a priori, a robot assistant must be able to localize and map its environment. The use of a camera sensor to solve localization has several advantages and weaknesses due the nature of the only-bearing data. But as the robot is expected to assist the human agent, this agent can deploy additional sensors and provide the robot with data to help solve the SLAM problem. Thus, another camera worn by the human agent is used to produce non-continuous stereo data with the robotic camera, to speed-up and add robustness to several parts of the monocular SLAM process considered. The approach has been tested with real experiments focused on singular trajectories and other issues found on industrial environments.