Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Curs acadèmic 2016-2017In the light of current advancement in deep learning, robot vision is not an exception. Many popular
machine learning algorithms has already been proposed and implemented to solve intricate computer
vision problems. The same has not been in the case of robot vision. Due to real time constraints and
dynamic nature of environment such as illumination and processing power, very few algorithms are
able to solve the object recognition problem at large.
The primary objective of the thesis project is to converge into an accurate working algorithm for
object recognition in a cluttered scene and subsequently helping the BAXTER robot to pick up
the correct object among the clutter. Feature matching algorithms usually fail to identify most of
the object having no texture, hence deep learning has been employed for better performance. The
next step is to look for the object and localize it within the image frame. Although basic shallow
Convolutional Neural Network easily identifies the presence of an object within a frame, it is very
difficult to localize the object location within the frame. This work primarily focuses on finding
a solution for an accurate localization. The first solution which comes to mind is to produce a
bounding box surrounding the object. In literature, YOLO is found to be providing a very robust
result on existing datasets. But this was not the case when it was tried on new objects belonging
to the current thesis project work. Due to high inaccuracy and presence of a huge redundant area
within the bounding box, an algorithm was needed which will segment the object accurately and
make the picking task easier. This was done through semantic segmentation using deep CNNs.
Although time consuming, RESNET has been found to be very efficient as its post processed output
helps to identify items in a significantly difficult task environment. This work has been done in light
of upcoming AMAZON robotic challenge where the robot successfully classified and distinguished
everyday items from a cluttered scenario. In addition to this, a performance analysis study has also
been done comparing YOLO and RESNET justifying the usage of the later algorithm with the help
of performance metrics such IOU and ViG
