Publications

Katsamenis I., Bimpas M., Protopapadakis E.,  Zafeiropoulos Ch., Kalogeras D.,  Doulamis A.,  Doulamis N., Montoliu C. M-P.,  Handanos Y., Schmidt F.,  Ott L.,  Cantero M.,  Lopez R. (2022).  Robotic Maintenance of Road Infrastructures: The HERON Project. Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece.

https://dl.acm.org/doi/10.1145/3529190.3534746

Abstract: Of all public assets, road infrastructure tops the list. Roads are crucial for economic development and growth, providing access to education, health, and employment. The maintenance, repair, and upgrade of roads are therefore vital to road users’ health and safety as well as to a well-functioning and prosperous modern economy. The EU-funded HERON project will develop an integrated automated system to adequately maintain road infrastructure. In turn, this will reduce accidents, lower maintenance costs, and increase road network capacity and efficiency. To coordinate maintenance works, the project will design an autonomous ground robotic vehicle that will be supported by autonomous drones. Sensors and scanners for 3D mapping will be used in addition to artificial intelligence toolkits to help coordinate road maintenance and upgrade workflows.

Zafeiropoulos Ch., Protopapadakis E., Chatzidaki A., Doulamis A., Vamvatsikos D., Zotos N., Bogdos G., Kostaridis A., Schmidt F., Ientile S., Sevilla I., Tilon S., Rallis I. (2022).  A holistic monitoring scheme for road infrastructures. Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece.

https://dl.acm.org/doi/abs/10.1145/3529190.3534745

Abstract: This monitoring system aims at increasing the resilience of the road infrastructures and ensuring reliable network availability under unfavourable conditions, such as extreme weather, landslides, and earthquakes. The main target is to combine downscaled climate change scenarios (applied to road infrastructures) with simulation tools (structural/geotechnical) and actual data (from existing and novel sensors), so as to provide the operators with an integrated tool able to support more effective management of their infrastructures at planning, maintenance and operation level. Towards this, the proposed framework aims to use high resolution modelling data for the determination and the assessment of the climatic risk of the selected, transport infrastructures and associated expected damages, use existing SHM data (from accelerometers, strain gauges etc.) with new types of sensor-generated data (computer vision) to feed the structural/geotechnical simulator, utilize tailored weather forecasts (combining seamlessly all available data sources) for specific hot-spots, providing early warnings with corresponding impact assessment in real time; develop improved multi-temporal, multi-sensor UAV, computer vision and machine learning-based damage diagnostic for diverse transport infrastructures; design and implement a Holistic Resilience Assessment Platform environment as an innovative planning tool that will permit a quantitative resilience assessment through an end-to-end simulation environment, running “what-if” impact/risk/resilience assessment scenarios. The effects of adaptation measures can be investigated by changing the hazard, exposure and vulnerability input parameters; design and implement a Common Operational Picture, including an enhanced visualisation interface and an Incident Management System. The integrated platform (and its sub-modules) will be validated in two real case studies in Spain and in Greece.

Katsamenis I., Karolou E.E., Davradou A., Protopapadakis E., Doulamis A., Doulamis N., Kalogeras D. (2022).  TraCon: A Novel Dataset for Real-Time Traffic Cones Detection Using Deep Learning. Proceedings of the 2nd International Conference (NiDS 2022), Athens, Greece.

https://doi.org/10.1007/978-3-031-17601-2_37

Abstract: Substantial progress has been made in the field of object detection in road scenes. However, it is mainly focused on vehicles and pedestrians. To this end, we investigate traffic cone detection, an object category crucial for road effects and maintenance. In this work, the YOLOv5 algorithm is employed, in order to find a solution for the efficient and fast detection of traffic cones. The YOLOv5 can achieve a high detection accuracy with the score of IoU up to 91.31%. The proposed method is been applied to an RGB roadwork image dataset, collected from various sources.

Katsamenis I., Davradou A., Karolou E.E., Protopapadakis E., Doulamis A., Doulamis N., Kalogeras D. (2022).  Evaluating YOLO Transferability Limitation for Road Infrastructures Monitoring. Proceedings of the 2nd International Conference (NiDS 2022), Athens, Greece.

https://doi.org/10.1007/978-3-031-17601-2_34

Abstract: Road infrastructure is positively associated with a country’s socio-economic growth and therefore road maintenance is of great importance for every country. One of the critical maintenance steps is road damage detection, which typically requires large amounts of time and high costs. In this work, the YOLOv5 two-stage detector is leveraged, in order to create an image-based solution for road defect detection and classification. The damages are classified into three main categories: cracks, potholes, and blurred markings. The YOLOv5 can achieve a relatively high detection accuracy with a score of Intersection over Union (IoU) up to 88.89% and classification accuracy with an F1 score up to 80.72%. The precision and recall scores are 84.26% and 78.38%, respectively.

Burnreiter L., Khattak S., Ott L., Siegwart R., Hutter M., Cadena C.. (2022).  Collaborative Robot Mapping using Spectral Graph Analysis. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, USA.

https://ieeexplore.ieee.org/document/9812102

Abstract: In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global map. However, inconsistencies between onboard and server estimates can occur due to onboard odometry drift or failure. Furthermore, robots do not benefit from the collaborative map if the server provides no feedback in a computationally tractable and bandwidth-efficient manner. Motivated by this challenge, this paper proposes a novel collaborative mapping framework to enable accurate global mapping among robots and server. In particular, structural differences between robot and server graphs are exploited at different spatial scales using graph spectral analysis to generate necessary constraints for the individual robot pose graphs. The proposed approach is thoroughly analyzed and validated using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90%.

Foster J., Ott L., Nieto J., Lawrance N., Siegwart R. (2023).  Automatic Extension of a Symbolic Mobile Manipulation Skill Set. Robotics and Autonomous Systems.

https://doi.org/10.1016/j.robot.2023.104428

Abstract: Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or tasks that are not captured by the initial description. We propose a method that allows an agent to automatically extend the abstract description of its skill set upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure computational efficiency. The resulting system is evaluated on a symbolic planning benchmark task and on object rearrangement tasks in simulation. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 25% higher success rate at a 67% faster runtime.