The latest automation trends are moving robotics away from traditional structured and predictable environments, to unstructured environments characterized by a higher degree of uncertainty and randomness. This thesis proposes the use of a 6D pose estimator based on point clouds in order to define the pose (position and orientation) of a large object in an unstructured environment. The proposed approach is based on the knowledge of the object's point cloud, obtainable from its CAD model, and on a real-time 3D scan of the environment in which the object is located. Through an extensive test phase, carried out both in a simulated and in a real life scenario, the performance of this approach was evaluated through the use of various properly defined KPIs.