A mobile camera is used to support an assisted teleoperation pipe–cutting system for nuclear decommissioning. The base system consists of dual–manipulators with a single mounted Kinect camera. The user selects the object from an on–screen image, whilst the computer control system automatically grasps the pipe with one end–effector and positions the second for cutting. However, the system fails in some cases because of data limitations, for example a partially obscured pipe in a challenging decommissioning scenario (simulated in the laboratory). Hence, the present article develops a new method to increase the use case scenarios via the introduction of mobile cameras e.g. for mounting on a drone. This is a non-trivial problem, with SLAM and ArUco fiducials introduced to locate the cameras, and a novel error correction method proposed for finding the ArUco markers. Preliminary results demonstrate the validity of the approach but improvements will be required for robust autonomous cutting. Hence, to reduce the pipe position estimation errors, suggestions are made for various algorithmic and hardware refinements.