Part Detection in Oneline-Reconstructed 3D Models.

Abstract: This thesis introduces a system to identify objects into a 3D reconstructed model. In particular, this is applied to automatize the inspection of an engine of a truck by detecting some parts in an online reconstructed 3D model. In this way, the work shows how the use of the augmented reality and the computer vision can be applied into a real application to automatize a task of inspection. To do this, the system employs the Signed Distance Function for the 3D representation which has been proven in other research as an efficient method for 3D reconstruction of environments. Then, some of the common processes for the recognition of shapes are applied to identify the pose of a specific part of the 3D model. This thesis explains the steps to achieve this task. The model is built using an industrial robot arm with a depth camera attached to the end effector. This allows taking snapshots from different viewpoints that are fused in a same frame to reconstruct the 3D model. The path for the robot is generated by applying translations to the initial pose of the end effector. Once the model is generated, the identification of the part is carried out. The reconstructed model and the model to be detected are analysed by detecting keypoints and features descriptors. These features can be computed together to obtain several instances over the target model, in this case the engine. Last, these instances can be filtered by the application of some constrains to get the true pose of the object over the scene. Last, some results are presented. The models were generated from a real engine truck. Then, these models were analysed to detect the oil filters by using different keypoint detectors. The results show that the quality of the recognition is good for almost all of the cases but it still presents some failures for some of the detectors. Keypoints too distinctive are more prune to produce wrong registrations due to the differences between the target and the scene. At the same time, more constrains make the detection more robust but also make the system less flexible.