Deep Learning, Machine Learning, Artificial Intelligence, Machine Vision
22
Scopus Publications
856
Scholar Citations
14
Scholar h-index
19
Scholar i10-index
Scopus Publications
SMapper: A Multi-Modal Data Acquisition Platform for SLAM Benchmarking Pedro Miguel Bastos Soares, Ali Tourani, Miguel Fernandez-Cortizas, Asier Bikandi-Noya, Holger Voos, Jose Luis Sanchez-Lopez Journal of Intelligent and Robotic Systems Theory and Applications, 2026 Advancing research in fields such as Simultaneous Localization and Mapping (SLAM) and autonomous navigation critically depends on the availability of reliable and reproducible multimodal datasets. While several influential datasets have driven progress in these domains, they often suffer from limitations in sensing modalities, environmental diversity, and the reproducibility of the underlying hardware setups. To address these challenges, this paper introduces SMapper, a novel open-hardware, multi-sensor platform designed explicitly for, though not limited to, SLAM research. The device integrates synchronized LiDAR, multi-camera, and inertial sensing, supported by a robust calibration and synchronization pipeline that ensures precise spatio-temporal alignment across modalities. Its open and replicable design allows researchers to extend its capabilities and reproduce experiments across both handheld and robot-mounted scenarios. To demonstrate its practicality, we additionally release SMapper-light, a publicly available SLAM dataset containing representative indoor and outdoor sequences. The dataset includes tightly synchronized multimodal data and ground truth trajectories derived from offline LiDAR-based SLAM with sub-centimeter accuracy, alongside dense 3D reconstructions. Furthermore, the paper contains benchmarking results on state-of-the-art LiDAR and visual SLAM frameworks using the SMapper-light dataset. By combining open-hardware design, reproducible data collection, and comprehensive benchmarking, SMapper establishes a robust foundation for advancing SLAM algorithm development, evaluation, and reproducibility. The project’s documentation, including source code, CAD models, and dataset links, is publicly available at https://snt-arg.github.io/smapper_docs/.
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics Ali Tourani, Deniz Işınsu Avşar, Hriday Bavle, Jose Luis Sanchez-Lopez, Jan P. F. Lagerwall, Holger Voos IEEE Robotics and Automation Magazine, 2026 Fiducial markers are widely used in robotics to support navigation, object recognition, and scene understanding. Despite their advantages in robotics and augmented reality (AR), their visibility can disrupt environmental esthetics, limiting their use in everyday settings. To address this gap, this article presents “iMarkers,” innovative and unobtrusive fiducial markers detectable exclusively by robots and AR devices equipped with adequate sensors and detection algorithms. These markers offer high flexibility in production, allowing customization of their visibility range and encoding algorithms to suit various demands. The article also introduces the hardware designs and open source software algorithms developed for detecting iMarkers, highlighting their adaptability and robustness in the detection and recognition stages. Various evaluations have demonstrated the effectiveness of iMarkers compared with conventional (printed) and blended fiducial markers and have confirmed their applicability across diverse robotics scenarios.
Interpretable Robot Control via Structured Behavior Trees and Large Language Models Ingrid Maéva Chekam, Ines Pastor-Martinez, Ali Tourani, Jose Andres Millan-Romera, Laura Ribeiro, Pedro Miguel Bastos Soares, Holger Voos, Jose Luis Sanchez-Lopez IEEE Access, 2025 As intelligent robots become more integrated into human environments, there is a growing need for intuitive and reliable Human-Robot Interaction (HRI) interfaces that are adaptable and more natural to interact with. Traditional robot control methods often require users to adapt to interfaces or memorize predefined commands, limiting usability in dynamic, unstructured environments. This paper presents a novel framework that bridges natural language understanding and robotic execution by combining Large Language Models (LLMs) with Behavior Trees. This integration enables robots to interpret natural language instructions given by users and translate them into executable actions by activating domain-specific plugins. The system supports scalable and modular integration, with a primary focus on perception-based functionalities, such as person tracking and hand gesture recognition. To evaluate the system, a series of real-world experiments was conducted across diverse environments. Experimental results demonstrate that the proposed approach is practical in real-world scenarios, with an average cognition-to-execution accuracy of approximately 94%, making a significant contribution to HRI systems and robots.
Pedestrian detection in low-light conditions: A comprehensive survey Bahareh Ghari, Ali Tourani, Asadollah Shahbahrami, Georgi Gaydadjiev Image and Vision Computing, 2024 Pedestrian detection remains a critical problem in various domains, such as computer vision, surveillance, and autonomous driving. In particular, accurate and instant detection of pedestrians in low-light conditions and reduced visibility is of utmost importance for autonomous vehicles to prevent accidents and save lives. This paper aims to comprehensively survey various pedestrian detection approaches, baselines, and datasets that specifically target low-light conditions. The survey discusses the challenges faced in detecting pedestrians at night and explores state-of-the-art methodologies proposed in recent years to address this issue. These methodologies encompass a diverse range, including deep learning-based, feature-based, and hybrid approaches, which have shown promising results in enhancing pedestrian detection performance under challenging lighting conditions. Furthermore, the paper highlights current research directions in the field and identifies potential solutions that merit further investigation by researchers. By thoroughly examining pedestrian detection techniques in low-light conditions, this survey seeks to contribute to the advancement of safer and more reliable autonomous driving systems and other applications related to pedestrian safety. Accordingly, most of the current approaches in the field use deep learning-based image fusion methodologies (i.e., early, halfway, and late fusion) for accurate and reliable pedestrian detection. Moreover, the majority of the works in the field (approximately 48%) have been evaluated on the KAIST dataset, while the real-world video feeds recorded by authors have been used in less than six percent of the works.
Vision-Based Situational Graphs Exploiting Fiducial Markers for the Integration of Semantic Entities Ali Tourani, Hriday Bavle, Deniz Işınsu Avşar, Jose Luis Sanchez-Lopez, Rafael Munoz-Salinas, Holger Voos Robotics, 2024 Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining geometric maps with diverse, hierarchically organized semantic entities and their topological relationships within one graph, but they also lead to improved performance of localization and mapping on the SLAM level by exploiting semantic information. In this paper, we introduce a vision-based version of S-Graphs where a conventional Visual SLAM (VSLAM) system is used for low-level feature tracking and mapping. In addition, the framework exploits the potential of fiducial markers (both visible and our recently introduced transparent or fully invisible markers) to encode comprehensive information about environments and the objects within them. The markers aid in identifying and mapping structural-level semantic entities, including walls and doors in the environment, with reliable poses in the global reference, subsequently establishing meaningful associations with higher-level entities, including corridors and rooms. However, in addition to including semantic entities, the semantic and geometric constraints imposed by the fiducial markers are also utilized to improve the reconstructed map’s quality and reduce localization errors. Experimental results on a real-world dataset collected using legged robots show that our framework excels in crafting a richer, multi-layered hierarchical map and enhances robot pose accuracy at the same time.
CAPRI: Context-aware point-of-interest recommendation framework[Formula presented] Ali Tourani, Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo Software Impacts, 2024 Point-of-Interest (POI) recommendation systems have gained popularity for their unique ability to suggest geographical destinations, with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework.
UAV-Assisted Visual SLAM Generating Reconstructed 3D Scene Graphs in GPS-Denied Environments Ahmed Radwan, Ali Tourani, Hriday Bavle, Holger Voos, Jose Luis Sanchez-Lopez 2024 International Conference on Unmanned Aircraft Systems Icuas 2024, 2024 Aerial robots play a vital role in various applications where situational awareness concerning the environment is a fundamental demand. As one such use case, drones in Global Positioning System (GPS)-denied environments require equipping with different sensors that provide reliable sensing results while performing pose estimation and localization. This paper aims to reconstruct maps of indoor environments and generate 3D scene graphs for a high-level representation using a camera mounted on a drone. Accordingly, an aerial robot equipped with a companion computer and an RGB-D camera was employed to be integrated with a Visual Simultaneous Localization and Mapping (VSLAM) framework proposed by the authors. To enhance situational awareness while reconstructing maps, various structural elements, i.e., doors and walls, were labeled with printed fiducial markers, and a dictionary of their topological relations was fed to the system. The system detects markers and reconstructs the map of the indoor areas enriched with higher-level semantic entities, including corridors and rooms. In this regard, integrating VSLAM into the employed drone provides an end-to-end robot application for GPS-denied environments that generates multi-layered vision-based situational graphs containing hierarchical representations. To demonstrate the system's practicality, various real-world condition experiments have been conducted in indoor scenarios with dissimilar structural layouts. Evaluations show the proposed drone application can perform adequately w.r.t. the ground-truth data and its baseline.
From SLAM to Situational Awareness: Challenges and Survey Hriday Bavle, Jose Luis Sanchez-Lopez, Claudio Cimarelli, Ali Tourani, Holger Voos Sensors, 2023 The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.
Marker-Based Visual SLAM Leveraging Hierarchical Representations Ali Tourani, Hriday Bavle, Jose Luis Sanchez-Lopez, Rafael Muñoz Salinas, Holger Voos IEEE International Conference on Intelligent Robots and Systems, 2023 Fiducial markers can encode rich information about the environment and aid Visual SLAM (VSLAM) approaches in reconstructing maps with practical semantic information. Current marker-based VSLAM approaches mainly utilize markers for improving feature detections in low-feature environments and/or incorporating loop closure constraints, generating only low-level geometric maps of the environment prone to inaccuracies in complex environments. To bridge this gap, this paper presents a VSLAM approach utilizing a monocular camera along with fiducial markers to generate hierarchical representations of the environment while improving the camera pose estimate. The proposed approach detects semantic entities from the surroundings, including walls, corridors, and rooms encoded within markers, and appropriately adds topological constraints among them. Experimental results on a real-world dataset collected with a robot demonstrate that the proposed approach outperforms a marker-based VSLAM baseline in terms of accuracy, given the addition of new constraints while creating enhanced map representations. Furthermore, it shows satisfactory results when comparing the reconstructed map quality to the one rebuilt using a LiDAR SLAM approach.
Passage-Aware Structural Mapping for RGB-D Visual SLAM A Tourani, M Fernandez-Cortizas, S Ejaz, DP Saura, A Bikandi-Noya, ... arXiv preprint arXiv:2604.24707 , 2026 2026
Applications of AI Algorithms for Eye Movement Analysis in Neurodevelopmental Disorders M Etemadi, N Esmi, A Tourani, A Shahbahrami, A Daemdoost 2026
Enhancing Robots’ Situational Awareness using Imperceptible Artificial Landmarks A TOURANI University of Luxembourg, Faculty of Science, Technology and Medicine … , 2026 2026
SMapper: A Multi-Modal Data Acquisition Platform for SLAM Benchmarking PM Bastos Soares, A Tourani, M Fernandez-Cortizas, A Bikandi-Noya, ... Journal of Intelligent & Robotic Systems , 2026 2026 Citations: 3
Situationally-aware path planning exploiting 3d scene graphs S Ejaz, M Giberna, M Shaheer, JA Millan-Romera, A Tourani, P Kremer, ... IEEE Robotics and Automation Letters , 2026 2026 Citations: 3
Environment-Aware 3D Scene Graphs for Visual SLAM via vS-Graphs A Tourani, S Ejaz, M FERNANDEZ CORTIZAS, JL SANCHEZ LOPEZ, ... International Conference on Robotics and Automation (ICRA) , 2026 2026
Interpretable Robot Control via Structured Behavior Trees and Large Language Models IM Chekam, I Pastor-Martinez, A Tourani, JA Millan-Romera, L Ribeiro, ... IEEE Access 13, 200905-200916 , 2025 2025 Citations: 2
Human Interaction for Collaborative Semantic SLAM using Extended Reality L Ribeiro, M Shaheer, M Fernandez-Cortizas, A Tourani, H Voos, ... arXiv preprint arXiv:2509.14949 , 2025 2025 Citations: 1
BIM Informed Visual SLAM for Construction Monitoring A Bikandi-Noya, M Fernandez-Cortizas, M Shaheer, A Tourani, H Voos, ... arXiv preprint arXiv:2509.13972 , 2025 2025 Citations: 2
ViLLA-MMBench: A Unified Benchmark Suite for LLM-Augmented Multimodal Movie Recommendation F Nazary, A Tourani, Y Deldjoo, T Di Noia arXiv preprint arXiv:2508.04206 , 2025 2025 Citations: 1
RAG-VisualRec: An Open Resource for Vision-and Text-Enhanced Retrieval-Augmented Generation in Recommendation A Tourani, F Nazary, Y Deldjoo arXiv preprint arXiv:2506.20817 , 2025 2025 Citations: 8
vs-graphs: Integrating visual slam and situational graphs through multi-level scene understanding A Tourani, S Ejaz, H Bavle, D Morilla-Cabello, JL Sanchez-Lopez, H Voos arXiv e-prints, arXiv: 2503.01783 , 2025 2025 Citations: 10
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics A Tourani, DI Avsar, H Bavle, JL Sanchez-Lopez, J Lagerwall, H Voos arXiv preprint arXiv:2501.15505 , 2025 2025 Citations: 4
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data A Tourani, S Ejaz, H Bavle, JL Sanchez-Lopez, H Voos arXiv preprint arXiv:2409.06625 , 2024 2024
Pedestrian detection in low-light conditions: A comprehensive survey B Ghari, A Tourani, A Shahbahrami, G Gaydadjiev Image and Vision Computing 148, 105106 , 2024 2024 Citations: 72
Vision-based situational graphs exploiting fiducial markers for the integration of semantic entities A Tourani, H Bavle, DI Avşar, JL Sanchez-Lopez, R Munoz-Salinas, ... Robotics 13 (7), 106 , 2024 2024 Citations: 12
Late Breaking Results on vS-Graphs: Integrating Visual SLAM and Situational Graphs for Multi-level Scene Understanding A Tourani, H Bavle, S Ejaz, D Morilla-Cabello, JL SANCHEZ LOPEZ, ... 2024 IEEE International Conference on Robotics and Automation (ICRA'24) , 2024 2024
CAPRI: Context-aware point-of-interest recommendation framework A Tourani, HA Rahmani, M Naghiaei, Y Deldjoo Software Impacts 19, 100606 , 2024 2024 Citations: 14
UAV-assisted visual SLAM generating reconstructed 3D scene graphs in GPS-denied environments A Radwan, A Tourani, H Bavle, H Voos, JL Sanchez-Lopez 2024 International Conference on Unmanned Aircraft Systems (ICUAS) , 2024 2024 Citations: 8
Late Breaking Results on Visual S-Graphs for Robust Semantic Scene Understanding and Hierarchical Representation A Tourani, H Bavle, JL SANCHEZ LOPEZ, H VOOS, R Munoz Salinas 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems … , 2023 2023 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Visual slam: What are the current trends and what to expect? A Tourani, H Bavle, JL Sanchez-Lopez, H Voos Sensors 22 (23), 9297 , 2022 2022 Citations: 168
From slam to situational awareness: Challenges and survey H Bavle, JL Sanchez-Lopez, C Cimarelli, A Tourani, H Voos Sensors 23 (10), 4849 , 2023 2023 Citations: 110
A robust deep learning approach for automatic Iranian vehicle license plate detection and recognition for surveillance systems A Tourani, A Shahbahrami, S Soroori, S Khazaee, CY Suen IEEE Access 8, 201317-201330 , 2020 2020 Citations: 108
Pedestrian detection in low-light conditions: A comprehensive survey B Ghari, A Tourani, A Shahbahrami, G Gaydadjiev Image and Vision Computing 148, 105106 , 2024 2024 Citations: 72
The unfairness of active users and popularity bias in point-of-interest recommendation HA Rahmani, Y Deldjoo, A Tourani, M Naghiaei International workshop on algorithmic bias in search and recommendation, 56-68 , 2022 2022 Citations: 53
Unclonable human-invisible machine vision markers leveraging the omnidirectional chiral Bragg diffraction of cholesteric spherical reflectors H Agha, Y Geng, X Ma, DI Avşar, R Kizhakidathazhath, YS Zhang, ... Light: Science & Applications 11 (1), 309 , 2022 2022 Citations: 40
Motion-based vehicle speed measurement for intelligent transportation systems A Tourani, A Shahbahrami, A Akoushideh, S Khazaee, CY Suen International Journal of Image, Graphics and Signal Processing 13 (4), 42 , 2019 2019 Citations: 40
Vehicle Counting Method Based on Digital Image Processing Algorithms AS Ali Tourani Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International … , 2015 2015 Citations: 36
A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm A Tourani, S Soroori, A Shahbahrami, S Khazaee, A Akoushideh 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) , 2019 2019 Citations: 27
Exploring the impact of temporal bias in point-of-interest recommendation HA Rahmani, M Naghiaei, A Tourani, Y Deldjoo Proceedings of the 16th ACM Conference on Recommender Systems, 598-603 , 2022 2022 Citations: 24
Marker-based visual slam leveraging hierarchical representations A Tourani, H Bavle, JL Sanchez-Lopez, RM Salinas, H Voos 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems … , 2023 2023 Citations: 18
Iranis: A large-scale dataset of iranian vehicles license plate characters A Tourani, S Soroori, A Shahbahrami, A Akoushideh 2021 5th International Conference on Pattern Recognition and Image Analysis … , 2021 2021 Citations: 18
A real-time license plate detection method using a deep learning approach S Khazaee, A Tourani, S Soroori, A Shahbahrami, CY Suen International Conference on Pattern Recognition and Artificial Intelligence … , 2020 2020 Citations: 18
Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways AJ Afshany, A Tourani, A Shahbahrami, S Khazaee, A Akoushideh International Journal of Intelligent Systems and Applications 11 (11), 25-37 , 2019 2019 Citations: 15
CAPRI: Context-aware point-of-interest recommendation framework A Tourani, HA Rahmani, M Naghiaei, Y Deldjoo Software Impacts 19, 100606 , 2024 2024 Citations: 14
Vision-based situational graphs exploiting fiducial markers for the integration of semantic entities A Tourani, H Bavle, DI Avşar, JL Sanchez-Lopez, R Munoz-Salinas, ... Robotics 13 (7), 106 , 2024 2024 Citations: 12
An accurate real-time license plate detection method based on deep learning approaches S Khazaee, A Tourani, S Soroori, A Shahbahrami, CY Suen International Journal of Pattern Recognition and Artificial Intelligence 35 … , 2021 2021 Citations: 12
vs-graphs: Integrating visual slam and situational graphs through multi-level scene understanding A Tourani, S Ejaz, H Bavle, D Morilla-Cabello, JL Sanchez-Lopez, H Voos arXiv e-prints, arXiv: 2503.01783 , 2025 2025 Citations: 10
Challenges of Video-Based Vehicle Detection and Tracking in Intelligent Transportation Systems A Tourani, A Shahbahrami, A Akoushideh International Conference on Soft Computing (ICSC) 2 , 2017 2017 Citations: 10
RAG-VisualRec: An Open Resource for Vision-and Text-Enhanced Retrieval-Augmented Generation in Recommendation A Tourani, F Nazary, Y Deldjoo arXiv preprint arXiv:2506.20817 , 2025 2025 Citations: 8
