Robust Apriltags Detector
Bachelor- or Master-Thesis at group TAMS
Motivation
Despite a lot of progress, robot vision algorithms are still not reliable
enough to detect and localize objects in a cluttered environments.
This motivated the development of so-called
fiducial marker systems,
whose specially designed patterns can be detected with specialized
computer vision algorithm.
For example, the popular Aruco and Apriltag markers consist of a simple
black and white rectangular pattern, that can simply be printed
and glued or pasted onto arbitrary objects.
The computer vision algorithm detects the outline of the pattern,
decodes the bit-pattern made up from the internal black and white patches,
and calculates distance and orientation of the marker from the camera.
The existing systems are highly optimized and are surprisingly robust
against different lighting conditions,
but detection quality deteriorates if the marker is too far away,
or if the camera or the object are moving.
Dataset Collection
In this thesis, we will set up one of the robot systems at TAMS
to record a dataset of Aruco/Apriltag or other fiducial markers
in different lighting conditions.
Mounting either the marker or the camera on the robot will allow us
to measure and vary the distance and orientation and the relative velocity
between camera and object.
Vision Algorithm
Using this dataset, you will then develop and implement improved vision
algorithm (classical or deep-learning) to detect the fiducials.
Several options look promising, and you can decide on which approach
you want to choose.
Software and ROS integration
For the dataset collection, we will select one of the robot arms
in the TAMS lab, using the ROS framework to generate and record
the robot motion, camera images, and required additional data.
The existing computer vision algorithms (Aruco and Apriltag) are
written in C/C++ and the OpenCV computer vision framework.
It would probably be easiest to develop the improved detector
on top of the existing implementation (using OpenCV bindings
from C/C++ and/or Python).
This would also allow to re-integrate the new algorithm into ROS.
However, other languages and programming environments could also be used.
Thesis Goals:
- using a robot+camera setup to record a new fiducial marker dataset
- develop ideas to improve the existing computer vision algorithms
- compare the new algorithm with the existing baselines
- if possible (Master-thesis) prepare a conference paper about this
Requirements
- as always, interest in the topic area
- willingness to work with a robot and to learn OpenCV
- basic knowledge of computer-vision in general
- basic knowledge of C/C++ or Python 3
- optionally, basic knowledge of deep-learning techniques
Contact
