3D data retrieval is required in various fields such as an industrial monitoring, agriculture, and robotics. Recent advances in photogrammetry and computer vision allowed to perform 3D reconstruction using a set of images captured with uncalibrated camera. Such technique is commonly known as Structure-from-Motion. In this paper, we propose a reinforcement learning framework RL3D for online strong camera configuration planning onboard of a mobile robot. The mobile robot consists of a skid-steered wheeled platform, a single-board computer and an industrial camera. Our aim is developing a model that plans a set of robot location that provide a strong camera configuration. We developed an environment simulator to train our RL3D framework. The simulator was implemented using a 3D model of the indoor scene and includes a model of robot’s dynamics. We trained our framework using the simulator and evaluated it using a virtual and real environments. The results of the evaluation are encouraging and demonstrate that the controller model successfully learns simple camera configurations such as a circle around an object.