IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 4

Monocular Visual Autonomous Landing System for Quadcopter Drones Using Software in the Loop
Figure 1.
Autonomous landing system pipeline with IBVS.
methods, such as feature-based extraction and description
or homography-based approaches [21], [22], are
commonly used to estimate the relative pose of the
vehicle with respect to a landing platform at a relative
low computational cost.
Spatial information can be extracted from natural and
artificial landmarks. In [6], the authors proposed the use of
natural landmarks for the detection and reconstruction of
landing sites based on the texture of the ground. Visualbased
SLAM techniques are also exploited to assemble
world's representations and find feasible landing spots for
the rotor-craft in unstructured environments as shown
in [11]. Similarly, the use of artificial landmarks can alleviate
the autonomous landing task by providing references
with known dimensions for detection and tracking over
several image frames [23].
The use of markers has been exploited to provide a
traceable reference for landing control systems and to
enhance the position estimation of aerial vehicles through
visual inertial odometry (VIO). In [7], the authors estimated
the relative pose of the aircraft with respect to a
spherical target and used an extended Kalman filter to
fuse these measurements with IMU data to accurately
locate the vehicle within the space.
Kalman filters are not exclusively employed to fuse
information from multiple sensor sources but also to estimate
the states of a system from a unique noisy
source [24], [25]. These estimations are used in IBVS,
with linear control strategies such as nested PIDs [6], [18]
and nonlinear ones like sliding-mode controllers [15], to
accurately land an aerial vehicle. The utilization of
Gaussian estimators for IBVS provides numerically
stable and continuous references for controllers, even
when the object of interest is outside the field of view of
the camera.
Further research efforts have concentrated on the use
of deep learning methods to detect and track landmarks in
images using CNN-based architectures [9], [26], [27] or
to automate the complete landing task with deep
4
reinforcement learning agents [28]. However, the use of
artificial neural networks requires substantial computational
resources for real-time inference and thousand of
human-labeled images based on the task at hand [9]. Likewise,
visual-based 3D reconstruction techniques tend to
be computationally expensive for on-board computers in
small UAVs [11], and need to satisfy various assumptions
to achieve accurate pose estimations.
We aim to reduce the computational load when performing
IBVS with the use of a vision-based tracking system,
and to produce a stable reference for a set of nested
PID-based controllers similar to those in [6]. The idea
behind the detection and tracking system is to produce a
2D image-based reference for the controller, thus avoiding
expensive 3D pose reconstructions as in [15]. In this
work, the use of the Kalman filter is restricted to filtering
2D estimations of the landing pad from noisy observations,
unlike the application of VIO in most other related
work. Contrary to commonly used simulation tools like
RotorS [17], which provide Gazebo-based simulation
environments for multirotor drones with no interface with
a real flight controller, our implementation utilises the
SITL provided by PX4, which runs the Pixhawk flight
stack and, therefore, provides direct support to the physical
robot deployment process.
VISION SYSTEM
This section describes our detection and tracking system
for the autonomous landing of a UAV, which is an extension
of our previous work in [21] and [33]. We first
explain how the feature-based object detector detects the
landing platform when comparing the platform's template
with the input image. Next, we describe how the
system translates the corners and the centroid of the
detected platform from the homography matrix to a vector
that contains the system observations. Finally, we
explain how a tailored Kalman filter is used to estimate
IEEE A&E SYSTEMS MAGAZINE
MAY 2022

IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV

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