2 research outputs found
Study and development of a reliable fiducials-based localization system for multicopter UAVs flying indoor
openThe recent evolution of technology in automation, agriculture, IoT, and aerospace fields
has created a growing demand for mobile robots capable of autonomous operation and
movement to accomplish various tasks. Aerial platforms are expected to play a central
role in the future due to their versatility and swift intervention capabilities. However,
the effective utilization of these platforms faces a significant challenge due to localization,
which is a vital aspect for their interaction with the surrounding environment.
While GNSS localization systems have established themselves as reliable solutions for
open-space scenarios, the same approach is not viable for indoor settings, where localization
remains an open problem as it is witnessed by the lack of extensive literature on
the topic.
In this thesis, we address this challenge by proposing a dependable solution for small
multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based
localization system reconstructs the pose by fusing data from onboard sensors. The primary
source of information stems from the recognition of AprilTags fiducial markers,
strategically placed in known positions to form a “map”.
Building upon prior research and thesis work conducted at our university, we extend
and enhance this system. We begin with a concise introduction, followed by a justification
of our chosen strategies based on the current state of the art. We provide an
overview of the key theoretical, mathematical, and technical aspects that support our
work. These concepts are fundamental to the design of innovative strategies that address
challenges such as data fusion from different AprilTag recognition and the elimination
of misleading measurements. To validate our algorithms and their implementation,
we conduct experimental tests using two distinct platforms by using localization
accuracy and computational complexity as performance indices to demonstrate the
practical viability of our proposed system.
By tackling the critical issue of indoor localization for aerial platforms, this thesis tries
to give some contribution to the advancement of robotics technology, opening avenues
for enhanced autonomy and efficiency across various domains.The recent evolution of technology in automation, agriculture, IoT, and aerospace fields
has created a growing demand for mobile robots capable of autonomous operation and
movement to accomplish various tasks. Aerial platforms are expected to play a central
role in the future due to their versatility and swift intervention capabilities. However,
the effective utilization of these platforms faces a significant challenge due to localization,
which is a vital aspect for their interaction with the surrounding environment.
While GNSS localization systems have established themselves as reliable solutions for
open-space scenarios, the same approach is not viable for indoor settings, where localization
remains an open problem as it is witnessed by the lack of extensive literature on
the topic.
In this thesis, we address this challenge by proposing a dependable solution for small
multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based
localization system reconstructs the pose by fusing data from onboard sensors. The primary
source of information stems from the recognition of AprilTags fiducial markers,
strategically placed in known positions to form a “map”.
Building upon prior research and thesis work conducted at our university, we extend
and enhance this system. We begin with a concise introduction, followed by a justification
of our chosen strategies based on the current state of the art. We provide an
overview of the key theoretical, mathematical, and technical aspects that support our
work. These concepts are fundamental to the design of innovative strategies that address
challenges such as data fusion from different AprilTag recognition and the elimination
of misleading measurements. To validate our algorithms and their implementation,
we conduct experimental tests using two distinct platforms by using localization
accuracy and computational complexity as performance indices to demonstrate the
practical viability of our proposed system.
By tackling the critical issue of indoor localization for aerial platforms, this thesis tries
to give some contribution to the advancement of robotics technology, opening avenues
for enhanced autonomy and efficiency across various domains
Tag-based Visual Odometry Estimation for Indoor UAVs Localization
The agility and versatility offered by UAV platforms still encounter
obstacles for full exploitation in industrial applications due to their indoor
usage limitations. A significant challenge in this sense is finding a reliable
and cost-effective way to localize aerial vehicles in a GNSS-denied
environment. In this paper, we focus on the visual-based positioning paradigm:
high accuracy in UAVs position and orientation estimation is achieved by
leveraging the potentials offered by a dense and size-heterogenous map of tags.
In detail, we propose an efficient visual odometry procedure focusing on
hierarchical tags selection, outliers removal, and multi-tag estimation fusion,
to facilitate the visual-inertial reconciliation. Experimental results show the
validity of the proposed localization architecture as compared to the state of
the art