Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
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.
2016. , 89 p.