Norbert Marko

58686158700

Publications - 9

Teaching Aspects of ROS 2 and Autonomous Vehicles †

Ernö Horváth Miklos Unger Rudolf Krecht Gergo Igneczi Norbert Marko

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

The advancement of autonomous vehicles (AVs) has brought forth a substantial need for effective education in robotic operating systems, particularly ROS 2, which serves as the backbone for many autonomous vehicle (AV) applications. This paper explores the academic approach and instructional methodologies tailored for teaching ROS 2 in the context of autonomous vehicle technology. It highlights the critical components and architecture of ROS 2, emphasizing its modularity, real-time communication capabilities, and robust ecosystem which make it ideal for AV development. Through a detailed curriculum outline, we describe hands-on learning activities, simulation-based exercises, and project-driven modules that facilitate deep understanding and practical skills acquisition. The effectiveness of these teaching methods is evaluated through a mixed-methods study involving student feedback, performance assessments, and project outcomes. Results indicate significant improvements in student comprehension and proficiency in both ROS 2 and autonomous vehicle systems. This research contributes to the body of knowledge by providing a comprehensive framework for educators to effectively teach ROS 2, thereby fostering the next generation of engineers proficient in developing and deploying autonomous vehicle technologies.

Open Access: Yes

DOI: 10.3390/engproc2024079049

Performance Analysis of Position Estimation and Correction Methods †

Krisztian Enisz Ernö Horváth Zoltán Szeli József Gábor Pázmány Norbert Marko

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

There are several global and local position estimation and refinement techniques based on the GNSS (Global Navigation Satellite System) and environmental monitoring (e.g., LIDAR, Light Detection and Ranging). These are usually based on a combination of multiple sensors using some form of sensor fusion, together with a filtering or observation technique. The behavior of these algorithms may vary depending on the applied sensor signals and on their accuracy under different environmental conditions and for different vehicle types. In the case of systems that also use GNSS signals, different procedures must also be prepared for signal dropouts and, in the worst case, drastic fluctuations in accuracy. The aim of this research is to present and compare the performance of different estimation procedures for different vehicles and environmental conditions.

Open Access: Yes

DOI: 10.3390/engproc2024079061

Inverse Perspective Mapping Correction for Aiding Camera-Based Autonomous Driving Tasks †

Péter Korös Miklos Unger Norbert Marko

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

Inverse perspective mapping (IPM) is a crucial technique in camera-based autonomous driving, transforming the perspective view captured by the camera into a bird’s-eye view. This can be beneficial for accurate environmental perception, path planning, obstacle detection, and navigation. IPM faces challenges such as distortion and inaccuracies due to varying road inclinations and intrinsic camera properties. Herein, we revealed inaccuracies inherent in our current IPM approach so proper correction techniques can be applied later. We aimed to explore correction possibilities to enhance the accuracy of IPM and examine other methods that could be used as a benchmark or even a replacement, such as stereo vision and deep learning-based monocular depth estimation methods. With this work, we aimed to provide an analysis and direction for working with IPM.

Open Access: Yes

DOI: 10.3390/engproc2024079067

Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis

Krisztian Enisz Ernö Horváth Istvan Szalay Norbert Marko

Publication Name: Machines

Publication Date: 2023-12-01

Volume: 11

Issue: 12

Page Range: Unknown

Description:

In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth.

Open Access: Yes

DOI: 10.3390/machines11121079

Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions

Tamás Budai Ernö Horváth Akos Kovacs Miklos Unger Rudolf Krecht Norbert Marko

Publication Name: IEEE Network

Publication Date: 2023-07-01

Volume: 37

Issue: 4

Page Range: 282-288

Description:

Global megatrends, such as urbanization, population growth, and emerging network solutions are accelerating the development of the Connected and Autonomous Vehicles (CAVs) industry. There are many truths, some misconceptions, and even some excitement about CAVs in the public's opinion. The main objective of the current article is to provide a comprehensive review, eliminate misconceptions, and outline the future of the network optimization aspects of autonomous vehicles by presenting various multidisciplinary methods, such as cooperative perception. Given our extensive experience with CAVs, we are aiming to share some of the insights and knowledge we have gained, along with relevant use-cases and experiment results.

Open Access: Yes

DOI: 10.1109/MNET.007.2300023

Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems

Áron Ballagi Norbert Marko Tamas Sziranyi

Publication Name: Iavvc 2023 IEEE International Automated Vehicle Validation Conference Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The prediction of terrain elevation values is a key task when it comes to off-road dynamics and inertial data estimation. A reliable elevation map can help in the estimation of future vehicle states and thus extend the response time window for autonomous navigation and control. We trained a deep learning model that is able to successfully predict top-down terrain depth maps in an off-road setting using a lightweight monocular depth estimation network. The labels were generated using a custom preprocessing algorithm to aid single image depth model training. Unlike other elevation estimation algorithms, our work can predict terrain variation from a higher camera setting without the use of a multi-sensor system. The network is also shown to work outside of the training data domain. The code will be available at https://www.github.com/norbertmarko/terrain-depth.

Open Access: Yes

DOI: 10.1109/IAVVC57316.2023.10328139

Monocular Curb Edge Detection via Robust Geometric Correspondences

Áron Ballagi Zoltan Rozsa Zoltan Rozsa Norbert Marko Tamas Sziranyi

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-12-01

Volume: 15

Issue: 24

Page Range: Unknown

Description:

Advanced driver-assistance and autonomous systems require perception that is both robust and affordable. Monocular cameras are promising due to their ubiquity and low cost, yet detecting abrupt road surface irregularities such as curbs and bumps remains challenging. These sudden road gradient changes are often only a few centimeters high, making them difficult to detect and resolve from a single moving camera. We hypothesize that stable image-based homography, derived from robust geometric correspondences, is a viable method for predicting sudden road surface gradient changes. To this end, we propose a monocular, geometry-driven pipeline that combines transformer-based feature matching, homography decomposition, temporal filtering, and late-stage IMU fusion. In addition, we introduce a dedicated dataset with synchronized camera and ground-truth measurements for reproducible evaluation under diverse urban conditions. We conduct a targeted feasibility study on six scenarios specifically recorded for small, safety-relevant discontinuities (four curb approaches, two speed bumps). Homography-based cues provide reliable early signatures for curbs (3/4 curb sequences detected at a 5 cm threshold). These results establish feasibility for monocular, geometric curb detection and motivate larger-scale validation. The code and the collected data will be made publicly available.

Open Access: Yes

DOI: 10.3390/app152412922

Robust Road Surface Normal and Pitch Prediction via IMU-Camera Fusion

Áron Ballagi Zoltan Rozsa Zoltan Rozsa Norbert Marko Tamas Sziranyi

Publication Name: Lecture Notes in Computer Science

Publication Date: 2026-01-01

Volume: 15656 LNCS

Issue: Unknown

Page Range: 591-603

Description:

Predicting road surface normal and pitch with image-based algorithms remains a significant challenge, especially when steep inclines, declines, and sudden changes in road inclination are involved. To solve this problem, we propose a novel image-based algorithm that leverages homography decomposition to achieve accurate ground plane normal and pitch prediction. By integrating a Kalman Filter, our method enhances prediction stability. Further, we incorporate robust sensor fusion by integrating IMU-based odometry, ensuring that the estimates are accurately aligned with real-world motion. Overall, our approach outperforms existing techniques in both accuracy and responsiveness for dynamic driving environments. Experimental results show that our approach achieves superior performance, reducing average pitch and normal errors by 0.493∘ and 0.483∘, respectively, compared to the current state-of-the-art, and exhibits a shorter transient response in case of a sudden road inclination change. Code is available at: https://norbertmarko.github.io/ground-normal-prediction/.

Open Access: Yes

DOI: 10.1007/978-3-032-07343-3_47

Monocular Ground Normal Prediction for the Road Ahead

Áron Ballagi Zoltan Rozsa Norbert Marko Tamas Sziranyi

Publication Name: IEEE Open Journal of Vehicular Technology

Publication Date: 2026-01-01

Volume: 7

Issue: Unknown

Page Range: 1066-1080

Description:

Robust fusion of monocular and inertial data has the potential to offer a low-cost alternative for ground surface normal prediction ahead, compared to more expensive sensors, such as LiDAR. Yet robust camera-based prediction remains challenging, particularly for steep grades and texture-poor, homogeneous road surfaces. To address these issues, we propose an enhanced monocular camera-IMU fusion pipeline incorporating a lightweight transformer-based feature matcher for improved correspondence accuracy, and robust temporal filtering, using a spherical linear interpolation (SLERP) filter, to enhance consistency and reduce drift. To enable rigorous benchmarking and reproducibility, we also standardize the evaluation protocol and release a novel dataset containing synchronized camera, LiDAR, and IMU-derived pose data, specifically captured across diverse incline and decline scenarios. Extensive continuous validation demonstrates that our method significantly improves both accuracy and temporal stability over existing approaches, setting a new state of the art for robust, continuous ground normal estimation ahead.

Open Access: Yes

DOI: 10.1109/OJVT.2026.3676610