# A Pose-Based Visual Servoing strategy for the autonomous landing of an airliner using a YOLOv11 model for runway detection

Sofiane Kraïem, Augustin Fuchs, Gustav Öman Lundin, Tudor Avarvarei, Cédric Seren, Mario Cassaro

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## ► To cite this version:

Sofiane Kraïem, Augustin Fuchs, Gustav Öman Lundin, Tudor Avarvarei, Cédric Seren, et al. A Pose-Based Visual Servoing strategy for the autonomous landing of an airliner using a YOLOv11 model for runway detection. EUCASS 2025 - European Conference for AeroSpace Sciences, Jun 2025, rome, Italy. 10.13009/EUCASS2025-305. [hal-05369066](https://hal.science/hal-05369066v1)

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https://hal.science/hal-05369066v1

Submitted on 17 Nov 2025

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# A Pose-Based Visual Servoing strategy for the autonomous landing of an airliner using a YOLOv11 model for runway detection

Sofiane Kraïem *† and Augustin Fuchs * and Gustav Öman Lundin * and Tudor Avarvarei *
and Cédric Seren * and Mario Cassaro *

*DTIS, ONERA, Université de Toulouse, 31000, Toulouse, France

name.surname@onera.fr

†Corresponding author

## Abstract

Enhancing autonomy and safety in civil aviation, particularly in GPS-denied contexts, can significantly benefit from exteroceptive sensors. Recent advances in vision-based solutions and computer vision using neural networks for detection and segmentation have shown promise in estimating aircraft pose relative to a desired runway. Additionally, dataset generation tools have recently been available, enabling the training of Neural Networks (NN) models for runway detection in images. In this paper, we propose an extension of our previously developed Pose-Based Visual Servoing (PBVS) solution for autonomous airliner landing. This enhancement involves incorporating Artificial Intelligence (AI)-based measurements into the estimation of the aircraft pose. Specifically, pixel coordinates of detected runway corners serve as AI-based measurements, leveraging their corresponding real-world positions available in the onboard navigation dataset. Our proposed control strategy draws inspiration from ILS, which estimate an aircraft's lateral and vertical deviations from the runway using visual measurements. This approach enables the utilization of existing ILS-certified autopilots. The paper initially presents the integration of AI-based measurements from image flows into the multi-sensor data fusion filter providing an estimation of lateral and longitudinal deviations of the aircraft from the runway of interest. Subsequently, it provides a detailed explanation of the pipeline deriving the AI-based measurements with a YOLOv11 model as well as some insights on the training process and choice of datatest. Then, the proposed PBVS is validated through multiple replayed and fully autonomous landings performed on a high level fidelity simulation platform.

## 1. INTRODUCTION

Among the various phases of flight, the landing phase remains one of the most challenging for fixed-wing aircraft. Recent research endeavors have focused on developing robust methodologies to assist pilots or enable the safe and autonomous landing of such aircraft. In the absence of high-precision ground-based systems, such as Instrument Landing System (ILS) radio emitters providing deviations relative to the ideal descent path, alternative approaches employing exteroceptive sensors have been investigated. Vision-based onboard sensors have emerged as a natural and cost-effective solution for estimating the relative position of the aircraft with respect to the destination runway during the Final Approach Segment (FAS) and the landing phase¹⁻⁶. Advancements in automatic landing technologies represent a significant step toward reducing pilot workload and enhancing the safety of civil aircraft.

Two primary approaches can be identified in vision-based control strategies: Pose-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS)⁷,⁸. IBVS formulates guidance objectives directly in the image plane, whereas PBVS relies on visual input to estimate the vehicle's pose relative to the target. IBVS offers flexibility regarding the knowledge of the target's characteristics, as its features can be defined in relative terms within the image⁹. This property imparts a high degree of robustness to 2D visual servoing, particularly with respect to camera calibration. The IBVS approach can be summarized as controlling the robot or vehicle so that the current image perception aligns with the desired image features. Despite its robustness, IBVS suffers from a well-known limitation: the potential generation of unsuitable or even singular 3D trajectories. Despite their robustness, IBVS methods pose challenges for certification and integration in civil aircraft applications, due to their novel guidance structures and added maintenance complexity. As a result, manufacturers often favor PBVS, which decouples control from vision and leverages certified guidance laws. However, PBVS requires greater prior knowledge of the 3D environment, such as calibrated cameras and navigation databases. Studies on the use of nonlinear observers to estimate flight-path deviations have concluded that it
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

is necessary to incorporate data from at least two points on the runway, or from its two sidelines, in order to main-
tain system observability during the landing phase¹⁰,¹¹. Recent studies have explored multi-sensor fusion techniques,
combining stereo vision with GPS/GNSS, inertial data, or thermal imaging⁶,¹²⁻¹⁵.

The growing interest in Artificial Intelligence (AI) technologies, along with recent advances in video object segmentation and tracking,¹⁶ has motivated aircraft manufacturers and research laboratories to explore these tools for aviation applications. For instance, Airbus¹ has been developing Neural Landing System algorithms to improve aircraft pose estimation by detecting runway corners using YOLO-based neural network models¹⁷. A major challenge in deploying such AI systems lies in the availability of relevant datasets. As emphasized by Linden et al.¹⁸, large and diverse datasets are crucial for effective model training. Indeed, the dataset has emerged as a pivotal factor, not only in unlocking superior performance but also in serving as a crucial benchmark for validating and comparing various models across the market. In the context of autonomous landing, a specific challenge arises from the scarcity of datasets comprising airport and runway data. Several obstacles contribute to this paucity, including the inherent difficulties in collecting real-world data due to restricted access to airport areas, the limited availability of software rendering tools that can provide realistically scaled and visually authentic representations of real-world environments. Given those difficulties, synthetic dataset have been shown to significantly enhance performance. The emergence of modern dataset generation tools has further facilitated the training of neural networks for runway detection tasks¹⁹,²⁰. Numerous recent studies have investigated neural network-based runway detection and segmentation techniques for estimating Unmaned Aircraft vehicle (UAV) pose and attitude during autonomous landing. An early approach by Oszust²¹ combined classical image processing and neural network segmentation with vanishing point detection to estimate altitude, distance to runway, and lateral deviation. More recently, Tripathi et al.²² proposed identifying runway thresholds, touchdown points, and edges as regions of interest, reconstructing their spatial relationship to the UAV’s center of gravity using stereo vision constraints. Real-time performance, robust detection under diverse weather and lighting conditions, and sufficient detection range are critical for the feasibility of autonomous landings. Chen et al.²³ addressed these challenges by introducing the efficient runway feature extractor, a deep Convolutional Neural Network (CNN) model that outputs both semantic segmentation and feature lines. Tsapparellas et al.²⁴ showed that deep learning techniques can outperform traditional computer vision in terms of computational efficiency and resilience to poor lighting. Similarly, Li et al.²⁵ developed a deep learning framework focused on runway edge detection for real-time applications. To enable onboard deployment, Dai et al.²⁶ designed a lightweight neural network optimized for embedded systems. To improve generalization across airports with differing runway appearances, Wang et al.²⁷ introduced a context enhancement module functioning as a band-pass filter. For nighttime or low-light conditions, Nowak et al.²⁸ proposed using precision approach path indicator lights as visual cues for longitudinal control during automatic landing, leveraging fuzzy logic-based controllers. Lin et al.²⁹ proposed a deep learning-based navigation system that integrates CNN-based runway detection and localization as inputs to a guidance control law, enabling the computation of lateral deviations from the runway centerline. Similarly, Tripathi et al.³⁰ used YOLO-based detection to define regions of interest and estimated pose via ORB-SLAM3 and Perspective-n-Point algorithms. Yu et al.³¹ enhanced pose accuracy by fusing vision and inertial data using a covariance feedback control-based Kalman filter, correcting pixel-level discrepancies between infrared runway features and geo-referenced projections. Bharti et al.³² introduced a vision-based landing system that combines YOLOv7 with a MobileNet-based regression model to estimate lateral and vertical deviations, slant distances, and altitudes without prior knowledge of the runway. Zhang et al.³³ addressed altitude estimation using a monocular camera and IMU fusion within a novel end-to-end deep network architecture. Ferreira et al.³⁴ demonstrated a system that tracks runway keypoints in video frames using Lo-RAT and estimates the camera pose via the Perspective-n-Point algorithm for accurate alignment during landing. This literature overview highlights the significant progress made in dataset generation for training neural network models to extract runway features from imagery. It also underlines the increasing effectiveness of real-time detection pipelines that support onboard estimation of aircraft pose and deviations relative to the runway which are necessary components for autonomous landing systems.

In this paper, we extend our previously developed PBVS strategy⁶ to achieve ILS-like measurement capabilities and performance. The original PBVS framework is enhanced by replacing traditional computer vision algorithms, which extract the runway centerline from images, with a deep NN pipeline. This pipeline detects geo-referenced runway corners, which are then integrated into a Vision-Aided Extended Kalman Filter (VAEKF) to estimate, at least partially, the aircraft state variables. These vision-based state estimates are used to reconstruct standardized ILS error signals, which are subsequently fed into the guidance control laws. This integration is enabled by leveraging specific navigation data, typically available through the Flight Management System. After presenting the adaptation of the PBVS strategy to incorporate AI-based measurements, we discuss the implementation of the complete pipeline to ensure acceptable performance across a range of weather conditions and in major commercial airports worldwide. Finally, the performance of the proposed control strategy is evaluated using a high-fidelity real-time simulation platform³⁵.

¹https://acubed.airbus.com/blog/wayfinder/how-wayfinder-is-using-neural-networks-for-vision-based-autonomous-landing
The paper is organized as follows: Section 2 presents the PBVS principle and the integration of AI-based measurements into the estimation algorithms. Section 3 introduces the deep NN pipeline used to detect runways in images and generate the AI-based measurements. Section 4 details the implementation of the overall strategy on the real-time simulation platform and analyzes the behavior and performance of the proposed control strategy. Finally, Section 5 concludes the paper with a summary and perspectives for future work.

## 2. Overall Pose Base Visual Servoing strategy

<figure><img src="image_3.png" /><figcaption>Figure 1: General block diagram of the PBVS software solution.</figcaption></figure>

The proposed PBVS strategy, extended from⁶ and depicted in Fig. 1, can be decomposed in: a sensor layer, a software layer and a system layer. The sensor layer is in charge of capturing all necessary raw data that guarantee to the global landing function a nominal behavior. The software layer aims at processing previous raw data in order to deliver reliable flight-path deviations viewed as reference inputs by the downstream layer. This layer gathers both image processing with runway detection algorithms, detailed in section 3, and the VAEKF algorithms detailed in this section. The system layer can be any aircraft autopilots to perform the final approach segment trajectory until the end of the landing flight phase. The PBVS architecture on which our solution is based presents some algorithmic and operational advantages compared to IBVS. The main advantage is the possibility to filter all signals processed by the computer vision algorithms using a multi sensor data fusion algorithm such as the VAEKF.⁶ The second advantage of the proposed PBVS architecture is the possibility to use any current and certified system layer since the upstream functional pipeline delivers the same flight-path deviations format (DEVLOC, DEVGLD) as for ILS-based autopilots, based on the appendix C of DO-253 official document³⁶ written by the Radio Technical Commission for Aeronautics. With an additional navigation database that gathers all necessary information related to many destination runways worldwide (glide path angle, threshold crossing-height, runway width and length, etc.), the lateral and vertical flight-path deviations, DEVLOC and DEVGLD respectively, can be computed as:

$$ \text{DEVLOC} = K_{\text{LOC}} \tan^{-1} \left( \mathbf{u}_{\text{lat}} \cdot \left( \mathbf{r}_{\text{A/C}}^{\text{ECEF}} - \mathbf{r}_{\text{0,LOC}}^{\text{ECEF}} \right) | \mathbf{u}_{\text{rwy}} \cdot \left( \mathbf{r}_{\text{A/C}}^{\text{ECEF}} - \mathbf{r}_{\text{0,LOC}}^{\text{ECEF}} \right) \right) \quad (1a) $$

$$ \text{DEVGLD} = K_{\text{GLD}} \tan^{-1} \left( \mathbf{u}_{\text{ver}} \cdot \left( \mathbf{r}_{\text{A/C}}^{\text{ECEF}} - \mathbf{r}_{\text{0, GLD}}^{\text{ECEF}} \right) d(\mathbf{r}_{\text{A/C}}^{\text{ECEF}}, \mathbf{r}_{\text{0, GLD}}^{\text{ECEF}}) \right) - 0.7 \quad (1b) $$

$$ d(\mathbf{r}_{\text{A/C}}^{\text{ECEF}}, \mathbf{r}_{\text{0, GLD}}^{\text{ECEF}}) = \sqrt{\left(\mathbf{u}_{\text{rwy}} \cdot \left(\mathbf{r}_{\text{A/C}}^{\text{ECEF}} - \mathbf{r}_{\text{0, GLD}}^{\text{ECEF}}\right)\right)^2 + \left(\mathbf{u}_{\text{lat}} \cdot \left(\mathbf{r}_{\text{A/C}}^{\text{ECEF}} - \mathbf{r}_{\text{0, GLD}}^{\text{ECEF}}\right)\right)^2} \quad (1c) $$

In Eq. (1a) and (1b), coefficients ($K_{\text{LOC}}$, $K_{\text{GLD}}$) correspond to constant quantities that are computed from the on board navigation database. The orthogonal triplet ($\mathbf{u}_{\text{rwy}}$, $\mathbf{u}_{\text{lat}}$, $\mathbf{u}_{\text{ver}}$) defines the runway frame considered for these calculations whose origin coincides with destination runway Landing Threshold Point (LTP). It is a local Cartesian frame who's three axes are centred and aligned along the runway ($X_{\text{RWY}}$), to the left ($Y_{\text{RWY}}$), and upwards ($Z_{\text{RWY}}$) respectively. Then, $\mathbf{r}_{\text{A/C}}^{\text{ECEF}}$ refers to the position vector of the aircraft expressed in the ECEF frame. Both $\mathbf{r}_{\text{0,LOC}}^{\text{ECEF}}$ and $\mathbf{r}_{\text{0, GLD}}^{\text{ECEF}}$ vectors designate constant reference positions whose coordinates are also given in the Earth-Centred Earth-Fixed (ECEF) frame.

### 2.1 Software Layer: Pose estimation

In order to reconstruct the input deviations in Eq. 1, only an estimation of the absolute position of the aircraft expressed in the ECEF frame, $\mathbf{r}_{\text{A/C}}^{\text{ECEF}}$, is necessary. We estimate the position vector, $\hat{\mathbf{r}}_{\text{A/C}}^{\text{ECEF}}$, using an augmented non-linear state space representation of the aircraft navigation dynamics, denoted by $M_{\text{NL}}$, derived from³⁷ which can be
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

formulated by:

$$ \mathcal{M}_{\text{NL}} : \left\{ \begin{array}{l} \dot{\mathbf{X}}(t) = f(\mathbf{X}(t), \mathbf{U}(t), \mathbf{n}_x) \\ \mathbf{Y}(t_k) = h(\mathbf{X}(t_k)) + \mathbf{n}_y \end{array} \right. \qquad (2) $$

The above formulation describes a stochastic continuous-time process model with a discrete-time observation model, and gaussian noise vectors $\mathbf{n}_x \sim N(0, Q)$ and $\mathbf{n}_y \sim N(0, R)$. In this paper, we consider the following state vector $\mathbf{X}$:

$$ \mathbf{X} = (V_N \quad V_E \quad V_z \quad \delta_E \quad \lambda_E \quad z \quad \psi \quad \theta \quad \phi)^T \qquad (3) $$

where: $(V_N \quad V_E \quad V_z)$ refers to the NED velocities of the aircraft; $(\delta_E \quad \lambda_E \quad z)$ corresponds to its geodetic latitude, longitude, and altitude (polar positioning coordinates in the ECEF frame); the triplet $(\psi \quad \theta \quad \phi)$ describes orientation of the aircraft body frame w.r.t. the local NED system of axes. The input vector is given by $\mathbf{U} = (n_x \quad n_y \quad n_z \quad p \quad q \quad r)^T$, where $n_x, n_y, n_z$ are the body frame longitudinal, lateral, and vertical load factors and $p, q, r$ are the body frame angular rates. The output measurement vector is defined as $\mathbf{Y}_m = (V_{NIRS} \quad V_{EIRS} \quad V_{ZIRS} \quad \delta_{EIRS} \quad \lambda_{EIRS} \quad z_{IRS} \quad \psi \quad \theta \quad \phi \{u_i \quad v_i\})^T$, where $u_i$ and $v_i$ are respectively the pixel coordinates of the $i^{th}$ detected runway corner. Note that several corners can be detected simultaneously, these are the appended in pairs $\{u_j, v_j\}$ to the end of the vector. To estimate the system state $\mathbf{X}(t)$, we use an EKF-based estimation scheme detailed in.⁶

Considering that all inputs $\mathbf{U}$ are available at sampling rate of $dt$ and a subset of $p$ output measurements contained in the vector $\mathbf{Z}$ ($p \le \text{dim}(\mathbf{Y}_m)$), and the latter are perturbed by uncorrelated and independent noises (i.e. the measurement noise covariance matrix is diagonal), the MDF filtering equation may read:⁶

$$ \begin{align} \dot{\mathbf{X}}_{k+1|k+1} &\approx \underbrace{\dot{\mathbf{X}}_{k|k} + f(\dot{\mathbf{X}}_{k|k}, \mathbf{U}(t_k))}_{\mathbf{X}_{k+1|k}} \cdot dt + \sum_{i=1}^{p} \mathbf{K}_{k+1}^{i} [\mathbf{Z}_{k+1}^{i} - g(\dot{\mathbf{X}}_{k+1|k})] \tag{4} \\ \mathbf{P}_{k+1|k+1} &= \underbrace{\mathbf{F}_k \mathbf{P}_{k|k} \mathbf{F}_k^T}_{\mathbf{P}_{k+1|k}} + \mathbf{G}_k \mathbf{Q} \mathbf{G}_k^T - \sum_{i=1}^{p} \mathbf{K}_{k+1}^{i} \mathbf{H}_{k+1}^{i,T} \mathbf{P}_{k+1|k} \nonumber \\ \mathbf{K}_{k+1}^{i} &= \mathbf{P}_{k+1|k} \mathbf{H}_{k+1}^{i,T} (\mathbf{H}_{k+1}^{i} \mathbf{P}_{k+1|k} \mathbf{H}_{k+1}^{i,T} + \mathbf{R}_{i,i})^{-1} \nonumber \end{align} $$

where the matrices $\mathbf{F}_k, \mathbf{G}_k$, and $\mathbf{H}_{k+1}^i$ are calculated as

$$ \mathbf{F}_k = \frac{\partial f(\mathbf{X}(t), \mathbf{U}(t), \mathbf{n}_x)}{\partial \mathbf{X}_k}_{|\dot{\mathbf{X}}_{k|k}, \mathbf{U}_k, \mathbf{0}}, \quad \mathbf{G}_k = \frac{\partial f(\mathbf{X}(t), \mathbf{U}(t), \mathbf{n}_x)}{\partial \mathbf{n}_x}_{|\dot{\mathbf{X}}_{k|k}, \mathbf{U}_k, \mathbf{0}}, \quad \mathbf{H}_{k+1} = \frac{\partial h(\mathbf{X}(t_k))^i}{\partial \mathbf{X}_{t_k}}_{|\dot{\mathbf{X}}_{k|k}, \mathbf{0}} \qquad (5) $$

As developed in section 3, the integration of vision-based information is performed thanks to the navigation dataset gathering ECEF coordinates of each detected points. Therefore, the Cartesian coordinates of these points w.r.t. the ECEF frame can be immediately calculated. Then, successive and multiple changes of coordinates between ECEF, local NED, aircraft body and camera frames (assuming a pinhole camera model) allow to deduce the coordinates of runways' corners $\overrightarrow{O_C A_i}$ vectors in the camera frame, $\mathcal{F}_C$. The coordinates of the runway corner $A_i$ can be written in a compact form of the whole change of coordinates in function of estimated aircraft state as:

$$ \mathbf{X}_C^{A_i} = \begin{bmatrix} \mathbf{X}_C^{A_i} \\ \mathbf{Y}_C^{A_i} \\ \mathbf{Z}_C^{A_i} \end{bmatrix} = - \mathbf{M}_{C \to B}^{\text{rot}} \left[ \mathbf{M}_{N \to B}^{\text{rot}} \{\psi, \theta, \phi\} \mathbf{M}_{E \to R'}^{A_i} \{\delta_{A_i}, \lambda_{A_i}\} (\mathbf{X}_E^{\text{CoM}} \{\delta_E, \lambda_E, h\} - \mathbf{X}_E^{A_i}) + \mathbf{T}_B \right] \qquad (6) $$

with $\mathbf{X}_E^{\text{CoM}}$ and $\mathbf{X}_E^{A_i}$ respectively the ECEF coordinates of the aircraft Center of Mass (CoM) and the runway corner $A_i$ defined as:

$$ \mathbf{X}_E^{\text{CoM}} = \begin{pmatrix} (N(\delta_E) + h) \cos \delta_E \cos \lambda_E \\ (N(\delta_E) + h) \cos \delta_E \sin \lambda_E \\ (N(\delta_E)(1 + e^2) + h) \sin \delta_E \end{pmatrix} \qquad (7a) $$

$$ \mathbf{X}_E^{A_i} = \begin{pmatrix} (N(\delta_{A_i}) + h) \cos \delta_{A_i} \cos \lambda_{A_i} \\ (N(\delta_{A_i}) + h) \cos \delta_{A_i} \sin \lambda_{A_i} \\ (N(\delta_{A_i})(1 + e^2) + h) \sin \delta_{A_i} \end{pmatrix} \qquad (7b) $$

$$ N(\delta) = \frac{a}{\sqrt{1 - (e \sin \delta)^2}} \qquad (7c) $$
with a the semimajor axis and e the eccentricity of the ellipse modeling the Earth's shape (The World Geodetic System 1984 provides numerical values). The $M_{E→R'}^{LTP}\{\delta_{A_i}, \lambda_{A_i}\}$ is the constant rotation matrix between the ECEF and the runway geographic frame, $R'$, given by:

$$ M_{E→R'}^{A_i}\{\delta_{A_i}, \lambda_{A_i}\} = \begin{bmatrix} -\sin \delta_{A_i} \cos \lambda_{A_i} & -\sin \delta_{A_i} \sin \lambda_{A_i} & \cos \lambda_{A_i} \\ -\sin \lambda_{A_i} & \cos \lambda_{A_i} & 0 \\ -\cos \delta_{A_i} \cos \lambda_{A_i} & -\cos \delta_{A_i} \sin \lambda_{A_i} & -\sin \delta_{A_i} \end{bmatrix} \quad (8) $$

$M_{R'→N}^{rot}\{\psi_{RWY}\}$ is the rotation matrix between the runway frame and the local NED frame, with $\psi_{RWY}$ the runway geographical heading (QFU). $M_{N→B}^{rot}\{\psi, \theta, \phi\}$ represents the rotation matrix that relates the A/C local NED frame to the A/C body frame, as detailed in.38 This matrix depends on the estimated states of the aircraft. Lastly, $M_{C→B}^{rot}$ and $T_B$ denote the fixed rotation matrix and translation vector between the A/C body frame and the CCS, respectively. The point $X_C^{A_i}$ is then expressed in the 2D image as:

$$ X_p^{A_i} = \begin{bmatrix} u_i \\ v_i \end{bmatrix} = \begin{bmatrix} f_x \frac{y_{C}^{A_i}}{x_{C}^{A_i}} + c_x \\ f_y \frac{z_{C}^{A_i}}{x_{C}^{A_i}} + c_y \end{bmatrix} \quad (9) $$

with $f_x$ and $f_y$ are the focal length of the camera and ($c_x, c_y$) the center of the camera in the image plane both obtained with the intrinsic camera calibration. This vision-based information can be merged to correct A/C predicted state $\hat{X}_{k+1|k}$.

## 2.2 System Layer: Guidance control laws

As proposed in$^6$, in order to perform an autonomous landing the aircraft keeps a constant altitude and speed while it begins the alignment to the runway by "capturing" the lateral deviation signal. When the aircraft is under 12 km from the runway, the aircraft guidance switches to a longitudinal deviation capture and tracking while the lateral deviation is captured and tracked all along the descent while maintaining an indicated airspeed. This translates into two guidance laws depending on the distance to the runway, providing to the inner loops the desired pitch, roll and thrust. Prior reaching a distance of 12 km, the guidance laws are:

$$ \phi_d = \mathbf{K}_\phi \text{DEVLOC} + \mathbf{D}_\phi \text{DEV'LOC} \quad (10a) $$

$$ \theta_d = \mathbf{K}_\theta (Z_d - Z) + \mathbf{D}_\theta (V_{Z_d} - V_Z) \quad (10b) $$

$$ u_{thr_d} = u_{thr_{trim}} \quad (10c) $$

with $u_{thr_{trim}}$ the necessary thrust to maintain a desired NED altitude, $Z_d$. $\mathbf{K}_\theta$, $\mathbf{K}_\phi$ and $\mathbf{D}_\theta$, $\mathbf{D}_\phi$ are respectively the constant proportional and derivative gains for the longitudinal/lateral guidance laws. Then for distances below 12 km, these last become:

$$ \phi_d = \mathbf{K}_\phi \text{DEVLOC} + \mathbf{D}_\phi \text{DEV'LOC} \quad (11a) $$

$$ \theta_d = -\mathbf{K}_\theta \text{DEVGLD} + \mathbf{D}_\theta \text{DEV'GLD} + \sigma_H (\mathbf{K}_{flare} \delta_g + \mathbf{D}_{flare} \dot{\delta}_g) \quad (11b) $$

$$ \sigma_H = \begin{cases} 1 & \text{if } \delta_g \le \delta_{flare} \\ 0 & \text{otherwise} \end{cases} \quad (11c) $$

$$ u_{thr_d} = \mathbf{K}_u (V_{IAS_d} - V_{IAS}) + \mathbf{D}_u (\dot{V}_{IAS_d} - \dot{V}_{IAS}) \quad (11d) $$

with $\mathbf{K}_u$ and $\mathbf{D}_u$ are respectively constant proportional and derivative gains. A flare phase is implemented in the longitudinal guidance law with the term $\sigma_H$ in Eq. (11b). $\delta_g$ represents the distance to the ground, while $\delta_{flare}$ signifies the distance to initiate the flare maneuver, which is typically set at 50 feet above the LTP. Additionally, different capturing and tracking dynamics of the lateral and longitudinal deviations are imposed.

## 3. AI-based computer vision algorithms for runway detection

The runway detection algorithm, referred to as the computer vision algorithm in Figure 1, is introduced in this section. It provides the VAEKF with the pixel coordinates corresponding to the four corners of the runway of interest. This detection pipeline, described in Figure 2, takes advantages on recent significant simplifications undergone on paradigms of detection and segmentation model training. It bases itself on a YOLOV11$^{17}$ model, which has introduced
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

<figure><img src="image_4.png" /><figcaption>Figure 2: Overall runway's corner detection pipeline framework</figcaption></figure>

a comprehensive Python toolbox that streamlines the entire process, encompassing dataset preparation, model training, fine-tuning, as well as metrics and evaluation tools for rigorous model validation. This integrated framework is primarily contingent upon the quality of the dataset employed¹⁸. In our approach, we capitalize on the accuracy and efficiency of the YOLO model, utilizing its output as a reliable measurement. The pipeline initially processes the raw mask measurement extracted from YOLO, which may vary in resolution depending on the model's configuration. Within the YOLO pipeline, the extracted estimation is grounded in the model's confidence levels. Specifically, a threshold of 60% confidence is applied to filter out less reliable data, retaining only the most probable mask. Consequently, the primary input to our pipeline is the filtered, confidence-based detection output from YOLO, unprocessed. In a subsequent stage, computer vision techniques are employed to refine the mask. The refined mask undergoes a quadratic polygonal approximation process, yielding a set of four key points that are then formed into a convex polygon. To ensure accurate labelling of each corner (top-left, top-right, bottom-right, and bottom-left), we employ a distance matching step with minimization. Specifically, a cost matrix is utilized to optimally assign each of the four extracted points from the mask to its corresponding corner of the image, thereby determining the most accurate associations based on minimized mismatch costs. This systematic approach guarantees consistent and correct assignment of labels to the polygon's corners, providing a robust geometric representation for subsequent processing stages. By constraining the mask to a fixed size with a limited number of points (i.e., the four runway corners), we effectively reduce the noise introduced by variable-sized masks, facilitating rapid processing and efficient extraction of labelled corner coordinates. Nevertheless, this approach does not inherently prevent occasional jumps or inconsistencies in the primary YOLO detection output, as no dedicated object tracking algorithm is integrated alongside the mask detection. Consequently, the pipeline's output should be viewed as a noisy measurement rather than a refined estimation. To address this, we rely on the subsequent input to our estimation filter, which is specifically designed to mitigate noise and yield more accurate estimates.

To achieve performance comparable to ILS CAT-III standards, the detection pipeline should be capable of detecting runways with at least 60% confidence from a distance of approximately 6 to 8 km within the approach cone, as described in Appendix C of³⁶. Furthermore, to enable landings at major commercial airports worldwide, the system must account for a wide range of tarmac colors and diverse environmental features surrounding runways, such as buildings, highways, vegetation, and bodies of water, which can significantly influence detection performance. As highlighted in our previous work⁶, sensitivity to brightness due to variations in time of day and weather conditions must also be addressed to ensure fully autonomous vision-based landings under real-world conditions. To address these challenges within our research framework, we adopted the BARS dataset¹⁹, which meets several key requirements. The dataset provides annotated runway images under a variety of lighting and weather conditions and offers high-quality visual renderings similar to those used in our simulation platform³⁵, which is based on Microsoft Flight Simulator 2020. Although the BARS dataset was generated using the X-Plane simulator via a different pipeline, it represents a promising resource for validating our approach, both within our simulation environment and, ultimately, against real-world data, thus helping to bridge the gap between synthetic and operational domains. The operational
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

<figure><img src="image_5.png" /><figcaption>Figure 3: Runway mask area in pixels as a function of distance to the runway for various input image resolutions.</figcaption></figure>

mechanics of the initial pipeline stage involve continuously feeding of high-definition images (1920 × 1080 pixels), captured at a fixed rate of 60 Hz from a nose-mounted camera, into the NN model. To ensure compatibility between the model and this high-frame-rate imagery, multiple input adjustment strategies were explored. Two primary approaches were considered: (1) modifying the input image via contrast enhancement techniques (e.g., grayscale conversion, sharpening), upsampling, or region-of-interest cropping; and (2) dynamically adjusting the input dimensions of the first layer of the YOLOv11 model based on the computed pixel size of the runway relative to the observation distance and camera resolution. While a combination of these methods could potentially enhance detection performance, this paper focuses solely on the second approach, in which the model input is specifically adapted to maximize detection efficacy. The selection of the model's input size represents a trade-off between achieving real-time performance and initiating continuous runway detection early in the descent. Larger input sizes increase detection accuracy at greater distances but also raise the computational load, as they require expanding the first convolutional layer, thus increasing inference time.

Observation conditions and the intrinsic characteristics of the runway target introduce several challenges for ac-
curate detection. In particular, the pixel footprint of the runway within an image, the quantity of image surface area that
the runway occupies, varies inversely with the distance from the imaging sensor, following an exponential-like decay.
To quantify this effect, we constructed a geometric model of a runway conforming to standard aerodrome specifica-
tions and simulated an approach trajectory. The simulation assessed the area of the runway mask as a function of the
camera's image resolution. During the final approach phase, the runway's pixel footprint at 3 km altitude exceeds that
at 6 km by more than a factor of four, growing from approximately 580 to 2,400 pixels. When the image width is con-
strained to 960 px, however, the runway coverage at 6 km further diminishes from 580 px to just 140 px. Consequently,
selecting an appropriate input resolution for the YOLO detector is critical, since inadequate pixel representation at
extended stand-off distances undermines reliable feature extraction. Moreover, as illustrated in Figure 3, the staircase
behavior in apparent runway observed size implies that forward motion on the order of hundreds of meters to thousand
of meters depending of the resolution of the image, is required to induce a perceptible shift of the runway pixels within
the image frame. This under-sampling phenomenon degrades the detector's ability to resolve the runway's geometric
features, leading to increased localization error and decreased detection confidence. Given these observations, it is
considered necessary to adjust the detector's input dimensions to align with the image resolution, thereby maximizing
detection precision. The final choice of NN input size will be further discussed in Section 4, as this trade-off must be
addressed during implementation and depends on the specific NN architecture used.

4. Implementation and results on a real-time simulation platform

The proposed PBVS strategy is validated on a high-fidelity, real-time simulation platform developed at ON-
ERA³⁵. The validation process is conducted in two stages: first, by replaying multiple landing scenarios to assess
open-loop performance; and second, by performing closed-loop simulations using the same platform. The software
and hardware architecture of the simulation platform is designed to ensure high-fidelity and tightly coupled interactions
between the flight dynamics and visual environment models. This setup enables comprehensive integration of state ob-
servation, computer vision processing, virtual sensor data fusion, and full aircraft control within a real-time framework.
For closed-loop simulations, a Boeing 747 mathematical model is employed as the test case. The simulator operates by
generating synthetic video streams using Microsoft Flight Simulator 2020 on a dedicated computing unit. This unit is
coupled with a Speedgoat real-time machine that computes the aircraft's dynamic evolution. A second computing unit
captures the video frames and runs the runway detection algorithm, while a third unit performs multi-sensor data fusion
using the VAEKF described in Section 2.1. In open-loop simulations, an ILS-like landing is generated by bypassing
the Speedgoat real-time machine. These simulations involve replaying recorded GPS data to drive the aircraft position
in Microsoft Flight Simulator, while inertial reference system (IRS) data is used to feed the VAEKF. This configuration
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

allows for evaluation of detection and estimation performance independent of real-time control dynamics.

The software layer, illustrated in Figure 1, is deployed as multiple Robot Operating System (ROS) nodes distributed across different computing units of the simulation platform. The detection algorithms run on a dedicated unit equipped with an RTX 3090 GPU. Leveraging this high computational capability, a YOLOv11 model¹⁷ is used to detect runways in images. The ROS node responsible for runway corner extraction first converts ROS Image sensor messages using the cvBridge library. The converted image is then processed by the neural network model using the Ultralytics and Torch Python libraries, executing inference on the GPU. The NN models are trained on 11,000 images from the BARS dataset¹⁹, which includes a wide variety of runway orientations, lighting conditions, and weather scenarios. All models follow a consistent training procedure: training between 100 and 150 epochs with an input stride of 1920 pixels using two RTX 3090 GPUs (24 GB each). Due to inconsistencies between the ground-truth runway corner coordinates in the world model and those in Microsoft Flight Simulator, as well as the lack of a dedicated validation dataset, NN models are assessed based on the performance of the complete detection and estimation pipeline, particularly with respect to aircraft pose estimation accuracy.

## 4.1 Open-Loop validation

<table><caption>Table 1: List of airport considered for Open-Loop validation</caption><thead><tr><td>Airport</td><td>Country</td><td>Code</td><td>Runway ID</td></tr></thead><tbody><tr><td>Basel-Mulhouse</td><td>France</td><td>LFSB</td><td>15</td></tr><tr><td>Toluca International Adolfo Lopez Mateos</td><td>Mexico</td><td>MMTO</td><td>15</td></tr><tr><td>Félix Eboué</td><td>France</td><td>SOCA</td><td>08</td></tr><tr><td>Châteauroux-Centre "Marcel Dassault"</td><td>France</td><td>LFLX</td><td>21</td></tr><tr><td>Tianjin Binhai International</td><td>China</td><td>ZBTJ</td><td>16L</td></tr></tbody></table>

Open-loop validations are conducted by replaying real-flight data on our simulation platform and comparing ground-truth lateral and longitudinal deviations with those estimated by the proposed detection and estimation pipeline. Landings are performed at the airports listed in Table 4.1, chosen for their diverse tarmac colors, surrounding environments (e.g., buildings, highways, vegetation, water), and varied approach profiles. This diversity aims to test the robustness of the estimation pipeline under different airport characteristics encountered globally. To assess sensitivity to lighting conditions, each landing is replayed at three times of day, 6 a.m., 12 p.m., and 7 p.m, to capture variations in image brightness and sun-facing direction.

Before evaluating estimation performance, we address the selection of the NN input image size. This selection must satisfy the dual requirement of enabling continuous runway detection from 6-8 km with a minimum confidence threshold of 60%, while maintaining an inference time compatible with the 40 Hz control update rate. Three YOLOv11 models are compared, each using one of the following input sizes: 960 × 960, 1440 × 1440, and 1920 × 1920 pixels. Performance is assessed across all airports and time slots in terms of inference time, confidence scores, and distance at first detection. Results are summarized in Table 2. As expected (cf. Section 3), larger input sizes enable earlier detections in the descent. For instance, using 1920 × 1920 images yields first detections approximately 2 km earlier than with 960 × 960, a crucial advantage in guidance planning. While computational load increases with image size, the detection pipeline was executed on a high-capacity GPU, making real-time inference at high resolution feasible within system constraints. Consequently, high-definition input is retained for further validations to maximize detection range and precision. This trend is confirmed in Figure 4, which compares detection confidence as a function of distance to the runway for landings at SOCA 08 across the three image sizes. A clear shift in the distance of first confident detection (exceeding 60%) is observed, with differences of up to 1 km between models. Additional insight is provided in Table 2, which examine corner displacement between successive frames. Although displacement magnitudes are similar across models, smaller input sizes result in greater relative error, reducing detection stability and accuracy. These observations further support selecting the 1920 × 1920 resolution, consistent with the native resolution of the onboard camera.

The second phase of open-loop validation evaluates the pose estimation performance using the selected high-resolution input. A key criterion here is robustness to brightness variations. As shown in Table 2 and Figure 4, detection quality varies with lighting conditions, leading to fluctuations in estimation performance. In this phase, the estimation pipeline fuses AI-derived vision measurements with GPIRS data as described in Section 2.1. Results show that the 1920 × 1920 input yields the lowest average estimation error, reinforcing the choice of input size. To further investigate brightness sensitivity, we focus on landings at two representative airports, SOCA 08 and ZBTJ 16L. These are chosen for their contrasting tarmac colors and environmental complexity, as illustrated in Figures 6 and 5. Figures 7 and 8 present comparisons between estimated and ground-truth deviations, along with the corresponding error profiles for the
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

different time of day. In both cases, estimation errors remain below one meter, demonstrating consistent performance across different conditions. The primary variation lies in the timing of the first detection, which is inherently tied to lighting and contrast sensitivity of the vision-based detection algorithm.

<table><caption>Table 2: Results of varying input sizes on performance metrics for all replayed landings and different lighting conditions, including average lateral and longitudinal errors.</caption><thead><tr><th>Input size</th><th>Day time</th><th>Average inference time (s)</th><th>Average probability (%)</th><th>Average first detection distance (km)</th><th>Average corner displacement (pixels/frame)</th><th>Average DEVLOC error (m)</th><th>Average DEVGLD error (m)</th></tr></thead><tbody><tr><td rowspan="3">960 × 960</td><td>7 am</td><td>0.088</td><td>81.9</td><td>1.87</td><td>4.8</td><td>-0.56</td><td>0.89</td></tr><tr><td>12 am</td><td>0.088</td><td>74.8</td><td>4.265</td><td>2.1</td><td>0.14</td><td>0.98</td></tr><tr><td>7 pm</td><td>0.082</td><td>69.6</td><td>3.121</td><td>1.8</td><td>0.13</td><td>0.79</td></tr><tr><td rowspan="3">1440 × 1440</td><td>7 am</td><td>0.088</td><td>76</td><td>3.119</td><td>2.1</td><td>-0.27</td><td>0.27</td></tr><tr><td>12 am</td><td>0.093</td><td>76.3</td><td>4.36</td><td>2.8</td><td>0.59</td><td>0.93</td></tr><tr><td>7 pm</td><td>0.089</td><td>80.4</td><td>3.989</td><td>2.2</td><td>-0.32</td><td>0.32</td></tr><tr><td rowspan="3">1920 × 1920</td><td>7 am</td><td>0.091</td><td>77.8</td><td>3.603</td><td>2.2</td><td>-0.36</td><td>0.3</td></tr><tr><td>12 am</td><td>0.089</td><td>75.9</td><td>5.132</td><td>3.5</td><td>0.37</td><td>2.47</td></tr><tr><td>7 pm</td><td>0.089</td><td>81</td><td>4.555</td><td>2.2</td><td>-0.32</td><td>0.57</td></tr></tbody></table>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 4: YOLOv11 detection confidence rates for the SOCA 08 runway as a function of distance to the runway, shown for each model and time of day. From left to right: 7 a.m., 12 p.m., and 7 p.m.</figcaption></figure>

## 4.2 Closed-Loop Validation

Autonomous landings on runways LFSB 15 and SOCA 08 were conducted using our real-time simulation platform³⁵ to assess the feasibility and robustness of the proposed PBVS strategy. In open-loop configurations, the runway detection and VAEKF pipeline demonstrated sensitivity to changes in brightness and lighting conditions. To evaluate its performance in a closed-loop setting, fully autonomous landings were performed at various times of day and under different weather scenarios. Due to the absence of real flight data for a Boeing 747, each vision-based autonomous landing was benchmarked against a nominal landing. The nominal landing is defined as an autonomous landing executed using the ILS autopilot guidance described in Section 2.2, relying on synthetic GPS-derived position data. Initial conditions for all simulated flights are summarized in Table 4.2. Figures 9 and 10 illustrate multiple landings under diverse environmental conditions, allowing for a comprehensive assessment across a range of visibility and illumination levels.

Figures 11 and 12 compare the lateral and longitudinal deviations of vision-based autonomous landings with those of the nominal GPS-based landings. These comparisons provide valuable insights into the performance and behavior of the PBVS approach. All vision-based landings were completed successfully, confirming the overall feasibility of the strategy. However, variations in closed-loop dynamics were observed, primarily influenced by the timing and quality of initial visual detection, and by the relatively slow control dynamics designed to recover both lateral runway alignment and descent slope. This effect is particularly evident in landings on LFSB 15, as shown in the left subplot of Figure 12. Lateral deviations gradually converge toward the runway centerline and the nominal trajectory. Nevertheless, the largest deviation errors, up to 15 and 20 meters, occurred under the darkest conditions (e.g., 6 a.m. and midnight on cloudy days). These values are significant, considering the runway width is only 45 meters. This observation is consistent with open-loop experiments, where lower illumination led to degraded estimation performance.
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

<figure><img src="image_9.png" /><figcaption>Figure 5: Snapshots of the replayed landing on the ZBTJ 16L runway under different visual conditions. From left to right: 6 a.m., 12 p.m., and 7 p.m.</figcaption></figure>

A similar trend is observed for landings on SOCA 08, illustrated in Figures 10 and 11. In these cases, first reliable detections generally occurred around 3 km from the runway threshold. Under reduced brightness, detection quality deteriorated, leading to less accurate pose estimates and deviations in the final approach trajectory. Nonetheless, the performance at SOCA 08 was comparatively more stable, with maximum lateral deviations limited to 7.5 meters, well within acceptable bounds for a commercial runway of 45 meters width. As in the open-loop experiments, brighter conditions tended to delay first detections and reduce their precision. In closed-loop scenarios, this diminished the controller's ability to effectively correct trajectory deviations. This phenomenon is particularly pronounced during landings with snowy visibility, where detections occurred only after the aircraft reached approximately 200 meters from the runway LTP. These results are not solely attributable to excessive image brightness but are also likely caused by a lack of similarly bright or snowy scenes in the training BARS dataset¹⁹ used for the NN model. This change in visual characteristics constitutes a domain shift, affecting the operational validity of the detection and estimation pipeline. Furthermore, the impact of tarmac color was evident, as the two airports had contrasting surfaces: LFSB features a light white tarmac, whereas SOCA has a darker one. As a result, brightness affected contrast, and therefore detection and estimation performance, differently at each site.

<table><caption>Table 3: Boeing 747 initial flying conditions for landings</caption><thead><tr><th>Runway</th><th>SOCA 08</th><th>LFSB 15</th></tr></thead><tbody><tr><td>Longitude, Latitude (°)</td><td>-52.48, 4.76</td><td>47.61, 7.51</td></tr><tr><td>Altitude - wgs (ft)</td><td>1988.2</td><td>1024</td></tr><tr><td>φ, θ, ψ (°)</td><td>-1, 1.6, 61.8</td><td>1, 1.6, 160</td></tr><tr><td>φ, θ, ψ (°/s)</td><td>2, -0.2, 0</td><td>3, -1, 0</td></tr><tr><td>Speed (kts)</td><td>250</td><td>250</td></tr></tbody></table>
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

<figure><img src="image_10.png" /><figcaption>Figure 6: Snapshots of the replayed landing on the SOCA 08 runway under different visual conditions. From left to right: 6 a.m., 12 p.m., and 7 p.m.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 7: Comparison of estimated deviations DEVLOC and DEVGLD (top subplots), along with the corresponding estimation errors as a function of the distance to the runway LTP (bottom subplots), for a landing replayed on the ZBTJ 16L runway at 7 a.m., 12 p.m., and 7 p.m., as illustrated in Figure 5.</figcaption></figure>

<caption>Figure 8: Comparison of estimated deviations DEVLOC and DEVGLD (top subplots), along with the corresponding estimation errors as a function of the distance to the runway LTP (bottom subplots), for a landing replayed on the SOCA 08 runway at 7 a.m., 12 p.m., and 7 p.m., as illustrated in Figure 6.</caption>

5. Conclusions

To conclude, this paper presented an extension of our previous PBVS strategy for autonomous approach and landing of an airliner. The proposed architecture is decomposed in an ILS-like guidance law supplied by a multi-sensor
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

<figure><img src="image_12.png" /><figcaption>Figure 9: Snapshots of the autonomous landing on the LFSB 15 runway under different visual conditions. From left to right: 6 a.m., 12 p.m., 7 p.m., cloud dark day, and snow.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Figure 10: Snapshots of the autonomous landing on the SOCA 08 runway under different visual conditions. From left to right: 6 a.m., 12 p.m., 7 p.m., cloud dark day, and snow.</figcaption></figure>

data fusion filter. The novelty lies in the integration of AI-based measurements in the filter to provide an estimation of lateral and longitudinal deviations of the aircraft w.r.t. the runway, replacing the center-line strategy proposed in our previous work. The pipeline of computer vision algorithms to detect the runways in the image is a subsequent contribution of this paper. After providing insights on the choice of NN model and its training to reach for ILS-like performances, the proposed control strategy has been implemented on our high-level fidelity simulation platform. Extensive open-loop and close-loop simulation have been conducted highlighting both the feasibility of the method as well as robustness towards a variety of visual conditions. Results have demonstrated the relevance of the proposed vision-based control strategy for civil aviation autonomous functions in forthcoming future.

Future work will focus on reaching real-time performances with embedability constraints as well as robustifying the runway detection pipeline to reduce the computational time burden while maintaining satisfying performances for ILS-like autonomous landing.

6. Acknowledgments

This work is supported by a French government grant managed by the National Research Agency under the
PBVS FOR AUTONOMOUS LANDING OF AN AIRLINER WITH A YOLOV11 MODEL

<figure><img src="image_14.png" /><figcaption>Figure 11: Comparison of estimated deviations DEVLOC and DEVGLD as a function of the distance to the runway LTP, for autonomous vision-based landings on the SOCA 08 runway under the visual conditions illustrated in Figure 10.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Figure 12: Comparison of estimated deviations DEVLOC and DEVGLD as a function of the distance to the runway LTP, for autonomous vision-based landings on the LFSB 15 runway under the visual conditions illustrated in Figure 9.</figcaption></figure>

France 2030 program with the reference ANR-23-DEGR-0001 – DeepGreen Project.

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