[journals]   [conferences/workshops]   [books]   [editorials]   [book chapters]   [theses]   [technical reports]

An always updated list of my publications can be found on Google Scholar.

Journals


  1. J. Ren, A. Mandal, R. El-khawaldeh, S. X. Leong, J. Hein, A. Aspuru-Guzik, L. Nalpantidis, and K. Darvish, “Context-aware computer vision for chemical reaction state detection,” Digital Discovery, 2026.
  2. F. Montello, R. Güldenring, S. Scardapane, and L. Nalpantidis, “A survey on dynamic neural networks: from computer vision to multi-modal sensor fusion,” arXiv preprint arXiv:2501.07451, 2025.
  3. B. Espejo-Garcia, R. Güldenring, L. Nalpantidis, and S. Fountas, “Foundation vision models in agriculture: Dinov2, LoRA and knowledge distillation for disease and weed identification,” Computers and Electronics in Agriculture, vol. 239, p. 110900, 2025.
  4. P. Schmidt and L. Nalpantidis, “Segmentation dataset for reinforced concrete construction,” Automation in Construction, vol. 171, p. 105990, 2025.
  5. J. Wu, J. Ren, O. Ravn, and L. Nalpantidis, “Implications for using digital twins to enhance safety in warehouse 4.0 design,” Chemical Engineering Transactions, vol. 116, pp. 1–6, 2025.
  6. J. Wu, J. Ren, O. Ravn, and L. Nalpantidis, “A risk-informed design framework for functional safety system design of human–robot collaboration applications,” Safety, vol. 11, no. 1, p. 24, 2025.
  7. D. Katsatos, P. Charalampous, P. Schmidt, I. Kostavelis, D. Giakoumis, L. Nalpantidis, and D. Tzovaras, “Semantic 3D reconstruction for volumetric modeling of defects in construction sites,” Robotics, vol. 13, no. 7, p. 102, 2024.
  8. I. Kostavelis, L. Nalpantidis, R. Detry, H. Bruyninckx, A. Billard, S. Christian, M. Bosch, K. Andronikidis, H. Lund-Nielsen, P. Yosefipor, et al., “Robétarmé project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills,” Open Research Europe, vol. 4, p. 4, 2024.
  9. R. Güldenring, R. Andersen, and L. Nalpantidis, “Zoom in on the plant: Fine-grained analysis of leaf, stem and vein instances,” IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1588 – 1595, 2024.
  10. W. Yang, J. K. Crone, C. R. Lønkjær, M. M. Ribo, S. Shan, F. D. Frumosu, D. Papageorgiou, Y. Liu, L. Nalpantidis, and Y. Zhang, “Vision-guided robotic automation of vat polymerization additive manufacturing production: design, calibration and verification,” Journal of Intelligent Manufacturing and Special Equipment, vol. 4, no. 2, pp. 85–98, 2023.
  11. D. Arapis, M. Jami, and L. Nalpantidis, “Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities,” Frontiers in Robotics and AI, vol. 10, 2023.
  12. J. Li, R. Güldenring, and L. Nalpantidis, “Real-time joint-stem prediction for agricultural robots in grasslands using multi-task learning,” Agronomy, vol. 13, no. 9, 2023.
  13. R. Güldenring, F. K. van Evert, and L. Nalpantidis, “RumexWeeds: a grassland dataset for agricultural robotics,” Journal of Field Robotics, vol. 40, pp. 1639–1656, 2023.
  14. W. Yang, J. K. Crone, C. R. Lønkjær, M. M. Ribo, S. Shan, F. D. Frumosu, D. Papageorgiou, Y. Liu, L. Nalpantidis, and Y. Zhang, “Vision-guided robotic automation of vat polymerization additive manufacturing production: design, calibration and verification,” Journal of Intelligent Manufacturing and Special Equipment, vol. 4, no. 2, pp. 85–98, 2023.
  15. J. Becktor, E. Boukas, and L. Nalpantidis, “Re-annotation of training samples for robust maritime object detection,” Machine Learning with Applications, p. 100411, 2022.
  16. R. E. Nielsen, D. Papageorgiou, L. Nalpantidis, B. T. Jensen, and M. Blanke, “Machine learning enhancement of manoeuvring prediction for ship digital twin using full-scale recordings,” Ocean Engineering, vol. 257, p. 111579, 2022.
  17. R. Güldenring and L. Nalpantidis, “Self-supervised contrastive learning on agricultural images,” Computers and Electronics in Agriculture, vol. 191, 2021.
  18. F. E. T. Schöller, L. Nalpantidis, and M. Blanke, “Buoy light pattern classification for autonomous ship navigation using recurrent neural networks,” IEEE Transactions on Intelligent Transportation Systems, 2021.
  19. J. B. Becktor, E. Boukas, M. Blanke, and L. Nalpantidis, “Reweighting neural network examples for robust object detection at sea,” Electronics Letters, vol. 57, no. 16, pp. 608–610, 2021.
  20. R. E. Andersen, L. Nalpantidis, O. Ravn, and E. Boukas, “Simultaneous regression-based spatial coverage estimation and object detection with deep learning,” Electronics Letters, vol. 57, no. 16, pp. 605–607, 2021.
  21. T. Kounalakis, G. Triantafyllidis, and L. Nalpantidis, “Deep learning-based visual recognition of rumex for robotic precision farming,” Computers and Electronics in Agriculture, vol. 165, 2019.
  22. T. Kounalakis, G. Triantafyllidis, and L. Nalpantidis, “Image-based recognition framework for robotic weed control systems,” Multimedia Tools and Applications, vol. 77, no. 8, pp. 9567–9594, 2018.
  23. A. S. Polydoros and L. Nalpantidis, “Survey of model-based reinforcement learning: Applications on robotics,” Journal of Intelligent and Robotic Systems, vol. 86, no. 2, pp. 153–173, 2017.
  24. V. Krüger, A. Chazoule, M. Crosby, A. Lasnier, M. R. Pedersen, F. Rovida, L. Nalpantidis, R. Petrick, C. Toscano, and G. Veiga, “A vertical and cyber-physical integration of cognitive robots in manufacturing,” Proceedings of the IEEE, vol. 104, no. 5, pp. 1114–1127, 2016.
  25. I. Kostavelis, E. Boukas, L. Nalpantidis, and A. Gasteratos, “Stereo based visual odometry for autonomous robot navigation,” International Journal of Advanced Robotic Systems, vol. 13, no. 1, 2016.
  26. M. R. Pedersen, L. Nalpantidis, R. S. Andersen, C. Schou, S. Bøgh, V. Krüger, and O. Madsen, “Robot skills for manufacturing: From concept to industrial deployment,” Robotics and Computer-Integrated Manufacturing, vol. 37, no. 282–291, 2015.
  27. I. Kostavelis, L. Nalpantidis, E. Boukas, M. Rodrigalvarez, I. Stamoulias, G. Lentaris, D. Diamantopoulos, K. Siozios, D. Soudris, and A. Gasteratos, “SPARTAN: Developing a vision system for future autonomous space exploration robots,” Journal of Field Robotics, vol. 31, no. 1, pp. 107–140, 2014.
  28. I. Kostavelis, L. Nalpantidis, and A. Gasteratos, “Collision risk assessment for autonomous robots by offline traversability learning,” Robotics and Autonomous Systems, vol. 60, no. 11, pp. 1367–1376, 2012.
  29. D. Chrysostomou, A. Gasteratos, L. Nalpantidis, and G. C. Sirakoulis, “Multi-view 3D scene reconstruction using ant colony optimization techniques,” Measurement Science and Technology, vol. 23, no. 11, 2012.
  30. C. Smith, Y. Karayiannidis, L. Nalpantidis, X. Gratal, P. Qi, D. V. Dimarogonas, and D. Kragic, “Dual arm manipulation - a survey,” Robotics and Autonomous Systems, vol. 60, no. 10, pp. 1340–1353, 2012.
  31. L. Nalpantidis, G. C. Sirakoulis, and A. Gasteratos, “Non-probabilistic cellular automata-enhanced stereo vision simultaneous localisation and mapping (SLAM),” Measurement Science and Technology, vol. 22, no. 11, 2011.
  32. L. Nalpantidis and A. Gasteratos, “Stereovision-based fuzzy obstacle avoidance method,” International Journal of Humanoid Robotics, vol. 8, no. 1, pp. 169–183, 2011.
  33. L. Nalpantidis, A. Amanatiadis, G. C. Sirakoulis, and A. Gasteratos, “Efficient hierarchical matching algorithm for processing uncalibrated stereo vision images and its hardware architecture,” IET Image Processing, vol. 5, no. 5, pp. 481–492, 2011.
  34. L. Nalpantidis and A. Gasteratos, “Biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence,” Robotics and Autonomous Systems, vol. 58, no. 5, pp. 457–464, 2010.
  35. L. Nalpantidis and A. Gasteratos, “Stereo vision for robotic applications in the presence of non-ideal lighting conditions,” Image and Vision Computing, vol. 28, no. 6, pp. 940–951, 2010.
  36. L. Nalpantidis, G. C. Sirakoulis, and A. Gasteratos, “Review of stereo vision algorithms: from software to hardware,” International Journal of Optomechatronics, vol. 2, no. 4, pp. 435–462, 2008.
  37. G. Fikos, L. Nalpantidis, and S. Siskos, “A compact APS with FPN reduction and focusing criterion using fgmos photocell,” Sensors and Actuators A:Physical, vol. 147, pp. 419–424, October 2008.

Conferences / Workshops

  1. C. K. Øhrstrøm, R. I. C. Muchacho, Y. Dong, F. Moumtzidellis, R. Güldenring, F. T. Pokorny, and L. Nalpantidis, “Where to attend: A principled vision-centric position encoding with parabolas,” arXiv preprint arXiv:2602.01418, 2026.
  2. C. K. Øhrstrøm, R. Güldenring, and L. Nalpantidis, “Spiking patches: Asynchronous, sparse, and efficient tokens for event cameras,” arXiv preprint arXiv:2510.26614, 2025.
  3. P. Schmidt, V. Belagiannis, and L. Nalpantidis, “Depth edge alignment loss: Dealing with depth in weakly supervised semantic segmentation,” arXiv preprint arXiv:2509.17702, 2025.
  4. F. Montello, R. Güldenring, and L. Nalpantidis, “Clustvit: Clustering-based token merging for semantic segmentation,” in 2026 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2026.
  5. J. Gregorek and L. Nalpantidis, “Steeredmarigold: Steering diffusion towards depth completion of largely incomplete depth maps,” in 2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 13304–13311, IEEE, 2025.
  6. J. Cervera, D. Arapis, and L. Nalpantidis, “Training-free optical identifier recognition for injection-molded components,” in 2025 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6, IEEE, 2025.
  7. V. Tzouras, L. Nalpantidis, and R. Güldenring, “From web data to real fields: Low-cost unsupervised domain adaptation for agricultural robots,” in Scandinavian Conference on Image Analysis, pp. 203–216, Springer Nature Switzerland Cham, 2025.
  8. J. Ren, N. Fisker-Bødker, R. Güldenring, J. H. Chang, T. Vegge, O. Ravn, and L. Nalpantidis, “Empowering lab education: Integrating a vision-based monitoring system with small-scale self-driving experiment platforms,” in 2025 IEEE/SICE International Symposium on System Integration (SII), pp. 1155–1162, IEEE, 2025.
  9. D. Schober, R. Güldenring, J. Love, and L. Nalpantidis, “Vision-based robot manipulation of transparent liquid containers in a laboratory setting,” in 2025 IEEE/SICE International Symposium on System Integration (SII), pp. 1193–1200, IEEE, 2025.
  10. S. Inoue, T. Baugerød, M. Kleist, H. Møller, N. J. Jacobsen, and L. Nalpantidis, “Zen wakarimasen - mobile robot raking a zen garden in an artistic installation,” in International Conference on Social Robotics, pp. 169–173, Springer Nature Singapore Singapore, 2024.
  11. J. Ren, J. Wu, O. Ravn, and L. Nalpantidis, “Safer human-robot collaborative environments through in-simulation risk estimation and function-centric hazard analysis,” in Olympus International Conference on Supply Chains, pp. 174–184, Springer Nature Switzerland Cham, 2024.
  12. P. Schmidt, D. Katsatos, D. Alexiou, I. Kostavelis, D. Giakoumis, D. Tzovaras, and L. Nalpantidis, “Towards autonomous shotcrete construction: Semantic 3D reconstruction for concrete deposition using stereo vision and deep learning,” in 41st International Symposium on Automation and Robotics in Construction, pp. 896–903, IEEE, 2024.
  13. W. Yang, J. K. Crone, C. R. Lønkjær, M. M. Ribo, S. Shan, F. D. Frumosu, D. Papageorgiou, Y. Liu, L. Nalpantidis, and Y. Zhang, “Vision-guided robotic automation of vat polymerization additive manufacturing production: design, calibration and verification,” Journal of Intelligent Manufacturing and Special Equipment, vol. 4, no. 2, pp. 85–98, 2023.
  14. J. Ren, J. Wu, O. Ravn, and L. Nalpantidis, “Functional requirements elicitation approach for the design and integration of robotic system for automation,” in International Conference on System Reliability and Safety Engineering (SRSE), (Beijing, China), 2023.
  15. J. Ren, L. Nalpantidis, N. A. Andersen, and O. Ravn, “Building digital twin of mobile robotics testbed using centralized localization system,” in International Conference on Control, Mechatronics and Automation (ICCMA), (Grimstad, Norway), 2023.
  16. R. E. Andersen, R. Güldenring, J. Ren, D. Arapis, P. Schmidt, O. Ravn, E. Boukas, and L. Nalpantidis, “Online learning for obstacle detection in construction for a multi-robot setting,” in IEEE International Conference on Imaging Systems and Techniques, (Copenhagen, Denmark), 2023.
  17. S. L. Alaguero, A. Chirtoaca, D. Chrysostomou, and L. Nalpantidis, “Communicating robot intentions: Usability study of a socially-aware mobile robot,” in IEEE International Conference on Imaging Systems and Techniques, (Copenhagen, Denmark), 2023.
  18. P. Schmidt, R. Güldenring, and L. Nalpantidis, “SIFT-guided saliency-based augmentation for weed detection in grassland images: Fusing classic computer vision with deep learning,” in International Conference in Computer Vision Systems (ICVS), (Vienna, Austria), 2023.
  19. D. Arapis, A. Vallone, M. Jami, and L. Nalpantidis, “Multi-task learning for industrial mobile robot perception using a simulated warehouse dataset,” in European Conference on Mobile Robots (ECMR), (Coimbra, Portugal), 2023.
  20. J. Becktor, F. Schöller, E. Boukas, and L. Nalpantidis, “Robust uncertainty estimation for classification of maritime objects,” in IEEE International Conference on Robotics and Automation (ICRA), (London, UK), 2023.
  21. D. Arapis, M. Jami, and L. Nalpantidis, “Bridging depth estimation and completion for mobile robots reliable 3D perception,” in International Conference on Robot Intelligence Technology and Applications, (Gold Coast, Australia), 2022.
  22. D. Arapis, M. Jami, and L. Nalpantidis, “Challenges in reliable 3D perception for industrial mobile robots,” in IROS Workshop on “Robotic Systems Integration for Supply Chain Workflows: Design, Deploy, Execute”, (Kyoto, Japan), 2022.
  23. J. Becktor, F. E. T. Schöller, E. Boukas, M. Blanke, and L. Nalpantidis, “Bolstering maritime object detection with synthetic data,” in IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles (CAMS), IFAC-PapersOnLine, 2022.
  24. M. Rudolph, Y. Dawoud, R. Güldenring, L. Nalpantidis, and V. Belagiannis, “Lightweight monocular depth estimation through guided decoding,” in IEEE International Conference on Robotics and Automation (ICRA), (Philadelphia, USA), 2022.
  25. E. Lopez, J. Dominguez, D. Abia, F. van Evert, A. Nieuwenhuizen, M. Sytsma, L. Nalpantidis, R. Güldenring, H. Koonstra, H. Pekkeriet, L. Struik, and J. Fradin, “GALIRUMI project: Galileo-assisted robot to tackle the weed rumex obtusifolius and increase the profitability and sustainability of dairy farming,” in ICRA Workshop on “Agricultural Robotics and Automation”, 2022.
  26. S. Lopez Alaguero, A. Chirtoaca, D. Chrysostomou, and L. Nalpantidis, “Lessons learned from user experiments with a socially-aware mobile robot,” in ICRA Workshop on “Social Robot Navigation: Advances and Evaluation”, 2022.
  27. R. Güldenring, E. Boukas, O. Ravn, and L. Nalpantidis, “Few-leaf learning: Weed segmentation in grasslands,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Prague, Czech Republic), 2021. (Finalist, Best Paper Award on Agri-Robotics)
  28. R. E. Andersen, L. Nalpantidis, and E. Boukas, “Vessel classification using a regression neural network approach,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Prague, Czech Republic), 2021.
  29. J. Hornauer, L. Nalpantidis, and V. Belagiannis, “Visual domain adaptation for monocular depth estimation on resource-constrained hardware,” in ICCV Workshop on Embedded and Real-World Computer Vision in Autonomous Driving, 2021.
  30. B. Kovács, A. D. Henriksen, J. D. Stets, and L. Nalpantidis, “Object detection on TPU accelerated embedded devices,” in 13th International Conference of Computer Vision Systems (ICVS), 2021.
  31. R. Kajatin and L. Nalpantidis, “Image segmentation of bricks in masonry wall using a fusion of machine learning algorithms,” in ICPR 2020 workshop on Pattern Recognition in Construction and the Built Environment (PRAConBE 2020), (Milan, Italy), 2020.
  32. R. E. Andersen, L. Nalpantidis, O. Ravn, and E. Boukas, “Investigating deep learning architectures towards autonomous inspection for marine classification,” in IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), (Abudhabi, UAE), 2020.
  33. J. B. Becktor, F. E. T. Schöller, E. Boukas, M. Blanke, and L. Nalpantidis, “Lipschitz constrained neural networks for robust object detection at sea,” in International Conference on Maritime Autonomous Surface Ship (ICMASS), (Ulsan, Korea), 2020.
  34. F. E. T. Schöller, M. Blanke, M. Plenge-Feidenhans’l, and L. Nalpantidis, “Vision-based object tracking in marine environments using features from neural network detections,” in IFAC World Congress, (Berlin, Germany), 2020.
  35. R. T. E. Dvinge, A. Stalmach, and L. Nalpantidis, “Connection-based Bluetooth mesh network as a low energy solution for off-grid data networks,” in 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2019.
  36. F. N. Rasmussen, S. T. Andersen, B. Grossmann, E. Boukas, and L. Nalpantidis, “Planar pose estimation using object detecion and reinforcement learning,” in 12th International Conference of Computer Vision Systems (ICVS), Lecture Notes in Computer Science, Springer, 2019.
  37. T. Kounalakis, L. Nalpantidis, G. Triantafyllidis, M. J. Malinowski, and L. Chelini, “A robotic system employing deep learning for visual recognition and detection of weeds in grasslands,” in IEEE International Conference on Imaging Systems and Techniques, (Krakow, Poland), 2018.
  38. J. Spranger, R. Buzatoiu, A. Polydoros, L. Nalpantidis, and E. Boukas, “Human-machine interface for remote training of robot tasks,” in IEEE International Conference on Imaging Systems and Techniques, (Krakow, Poland), 2018.
  39. P. Valentin, T. Kounalakis, and L. Nalpantidis, “Weld classification using gray level co-occurrence matrix and local binary patterns,” in IEEE International Conference on Imaging Systems and Techniques, (Krakow, Poland), 2018.
  40. M. Kapoor, E. Katsanos, S. Thöns, L. Nalpantidis, and J. Winkler, “Structural integrity management with unmanned aerial vehicles: State-of-the-art review and outlook,” in Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE 2018), (Ghent, Belgium), 2018.
  41. E. Boukas, A. S. Polydoros, G. Visentin, L. Nalpantidis, and A. Gasteratos, “Global localization for future space exploration rovers,” in International Conference in Computer Vision Systems (ICVS), vol. 10528 of LNCS, (Hong Kong), Springer, 2017.
  42. T. Kounalakis, G. Triantafyllidis, and L. Nalpantidis, “Vision system for robotized weed recognition in crops and grasslands.,” in International Conference in Computer Vision Systems (ICVS), vol. 10528 of LNCS, (Hong Kong), Springer, 2017.
  43. A. S. Polydoros, E. Boukas, and L. Nalpantidis, “Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Vancouver, Canada), 2017.
  44. F. Rovida, V. Krueger, L. Nalpantidis, A. Charzoule, A. Lasnier, R. Petrick, M. Crosby, C. Toscano, and G. Veiga, “A cyber-physical systems approach for controlling autonomous mobile manipulators,” in Proceedings of the International Conference on Climbing and Walking Robots and Support Technologies for Mobile Machines (CLAWAR),(London, UK), pp. 169–177, 2016.
  45. A. S. Polydoros and L. Nalpantidis, “A reservoir computing approach for learning forward dynamics of industrial manipulators,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Daejeon, Korea), 2016.
  46. T. Kounalakis, G. Triantafyllidis, and L. Nalpantidis, “Weed recognition framework for robotic precision farming,” in IEEE International Conference on Imaging Systems and Techniques, (Chania, Greece), 2016.
  47. A. S. Polydoros, B. Gromann, F. Rovida, L. Nalpantidis, and V. Krüger, “Accurate and versatile automation of industrial kitting operations with SkiROS,” in 17th Conference Towards Autonomous Robotic Systems (TAROS), (Sheffield, UK), 2016.
  48. N. Rofalis, L. Nalpantidis, N. A. Andersen, and V. Krüger, “Vision-based robotic system for object agnostic placing operations,” in International Conference on Computer Vision Theory and Applications (VISAPP), (Rome, Italy), 2016.
  49. A. S. Polydoros, L. Nalpantidis, and V. Krüger, “Advantages and limitations of reservoir computing on model learning for robot control,” in IROS Workshop on Machine Learning in Planning and Control of Robot Motion, (Hamburg, Germany), 2015.
  50. A. S. Polydoros, L. Nalpantidis, and V. Krüger, “Real-time deep learning of robotic manipulator inverse dynamics,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Hamburg, Germany), 2015.
  51. G. Lentaris, I. Stamoulias, D. Diamantopoulos, K. Maragos, K. Siozios, D. Soudris, M. A. Rodrigalvarez, M. Lourakis, X. Zabulis, I. Kostavelis, L. Nalpantidis, E. Boukas, and A. Gasteratos, “SPARTAN/SEXTANT/COMPASS: advancing space rover vision via reconfigurable platforms,” in 11th International Symposium on Applied Reconfigurable Computing (ARC), vol. 9040 of Lecture Notes in Computer Science, (Bochum, Germany), pp. 475–486, Springer, 2015.
  52. B. Großmann, M. R. Pedersen, J. Klonovs, D. Herzog, L. Nalpantidis, and V. Krüger, “Communicating unknown objects to robots through pointing gestures,” in 15th Conference Towards Autonomous Robotic Systems (TAROS), vol. 8717 of Lecture Notes in Computer Science, (Birmingham, UK), pp. 209–220, Springer-Verlag, 2014.
  53. A. S. Polydoros, L. Nalpantidis, and V. Krüger, “Towards an intelligent robotic manipulator for industrial object-placing tasks,” in IAS International Workshop on Intelligent Robot Assistants, (Padova, Italy), 2014.
  54. A. S. Polydoros, L. Nalpantidis, and V. Krüger, “A roadmap towards intelligent and autonomous object manipulation for assembly tasks,” in ICRA Workshop on “Autonomous Grasping and Manipulation: An Open Challenge”, (Hong Kong), 2014.
  55. L. Nalpantidis, D. Kragic, I. Kostavelis, and A. Gasteratos, “Theta-disparity: an efficient representation of the 3D scene structure,” in 13th International Conference on Intelligent Autonomous Systems (IAS), Lecture Notes in Computer Science, (Padova, Italy), 2014.
  56. J. Klonovs, D. Herzog, M. R. Pedersen, B. Großmann, L. Nalpantidis, and V. Krüger, “Robotic system capable of identifying objects indicated by pointing gestures,” in Proceedings of the 2nd AAU Workshop on Robotics, (Aalborg, Denmark), AAU Press, 2014.
  57. N. Skordilis, N. Vidakis, G. Triantafyllidis, and L. Nalpantidis, “Depth camera driven mobile robot for human localization and following,” in Proceedings of the 2nd AAU Workshop on Robotics, (Aalborg, Denmark), AAU Press, 2014.
  58. R. S. Andersen, L. Nalpantidis, V. Krüger, O. Madsen, and T. B. Moeslund, “Using robot skills for flexible reprogramming of pick operations in industrial scenarios,” in International Conference on Computer Vision Theory and Applications (VISAPP), vol. 3, (Lisbon, Portugal), pp. 678–685, 2014.
  59. M. R. Pedersen, L. Nalpantidis, A. Bobick, and V. Krüger, “On the integration of hardware-abstracted robot skills for use in industrial scenarios,” in IROS Workshop on “Cognitive Robotics Systems: Replicating Human Actions and Activities”, (Tokyo, Japan), 2013.
  60. I. Kostavelis, E. Boukas, L. Nalpantidis, and A. Gasteratos, “Visual odometry for autonomous robot navigation through efficient outlier rejection,” in IEEE International Conference on Imaging Systems and Techniques, (Beijing, China), IEEE, October 2013.
  61. L. Nalpantidis, B. Großmann, and V. Krüger, “Fast and accurate unknown object segmentation for robotic systems,” in International Symposium on Visual Computing (ISVC), vol. 8034 of Lecture Notes in Computer Science, (Rethymnon, Greece), Springer, July 2013.
  62. G. Lentaris, D. Diamantopoulos, K. Siozios, I. Stamoulias, I. Kostavelis, E. Boukas, L. Nalpantidis, D. Soudris, A. Gasteratos, and M. A. Aviles, “SPARTAN: efficient implementation of computer vision algorithms for autonomous rover navigation,” in 7th HiPEAC Workshop on Reconfigurable Computing, (Berlin, Germany), European Network of Excellence on High Performance and Embedded Architecture and Compilation, January 2013.
  63. I. Kostavelis, A. Gasteratos, E. Boukas, and L. Nalpantidis, “Learning the terrain and planning a collision-free trajectory for indoor post-disaster environments,” in IEEE International Symposium on Safety, Security and Rescue Robotics, (College Station, Texas, USA), November 2012.
  64. L. Nalpantidis, M. Björkman, and D. Kragic, “Yes - yet another object segmentation: exploiting camera movement,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Vilamoura, Algarve, Portugal), 2012.
  65. I. Kostavelis, E. Boukas, L. Nalpantidis, and A. Gasteratos, “Path tracing on polar depth maps for robot navigation,” in Cellular Automata for Research and Industry (ACRI), (Santorini, Greece), 2012.
  66. E. Boukas, I. Kostavelis, L. Nalpantidis, and A. Gasteratos, “Graph based localisation refinement by orbital images,” in International Symposium on Artificial Intelligence, Robotics and Automation in Space, (Turin, Italy), 2012.
  67. I. Kostavelis, L. Nalpantidis, and A. Gasteratos, “Object recognition using saliency maps and HTM learning,” in IEEE International Conference on Imaging Systems and Techniques, (Manchester, United Kingdom), 2012.
  68. I. Kostavelis, E. Boukas, L. Nalpantidis, A. Gasteratos, and M. Aviles Rodrigalvarez, “SPARTAN system: Towards a low-cost and high-performance vision architecture for space exploratory rovers,” in 2nd International Workshop on Computer Vision in Vehicle Technology: From Earth to Mars, in conjunction with ICCV, (Barcelona, Spain), November 2011.
  69. M. Aviles, K. Siozios, D. Diamantopoulos, L. Nalpantidis, I. Kostavelis, E. Boukas, D. Soudris, and A. Gasteratos, “A co-design methodology for implementing computer vision algorithms for rover navigation onto reconfigurable hardware,” in FPL 2011 workshop on Computer Vision on Low-Power Reconfigurable Architectures, (Chania, Greece), September 2011.
  70. I. Kostavelis, L. Nalpantidis, and A. Gasteratos, “Supervised traversability learning for robot navigation,” in 12th Conference Towards Autonomous Robotic Systems, vol. 6856 of Lecture Notes in Computer Science, (Sheffield, UK), pp. 289–298, Springer-Verlag, 2011.
  71. K. Siozios, D. Diamantopoulos, I. Kostavelis, E. Boukas, L. Nalpantidis, D. Soudris, A. Gasteratos, M. Aviles, and I. Anagnostopoulos, “SPARTAN project: Efficient implementation of computer vision algorithms onto reconfigurable platform targeting to space applications,” in 6th International Workshop on Reconfigurable Communication-centric Systems-on-Chip, (Montpellier, France), pp. 1–9, June 2011.
  72. L. Nalpantidis, J. Kalomiros, and A. Gasteratos, “Robust 3D vision for robots using dynamic programming,” in IEEE International Conference on Imaging Systems and Techniques, (Batu Ferringhi, Penang, Malaysia), pp. 89–93, May 2011.
  73. D. Chrysostomou, L. Nalpantidis, and A. Gasteratos, “Lighting compensating multiview stereo,” in IEEE International Conference on Imaging Systems and Techniques, (Batu Ferringhi, Penang, Malaysia), pp. 176–179, May 2011.
  74. L. Nalpantidis, G. C. Sirakoulis, A. Carbone, and A. Gasteratos, “Computationally effective stereovision SLAM,” in IEEE International Conference on Imaging Systems and Techniques, (Thessaloniki, Greece), pp. 453–458, July 2010.
  75. I. Kostavelis, L. Nalpantidis, and A. Gasteratos, “Comparative presentation of real-time obstacle avoidance algorithms using solely stereo vision,” in IARP/EURON International Workshop on Robotics for risky interventions and Environmental Surveillance-Maintenance, (Sheffield, UK), January 2010.
  76. L. Nalpantidis, D. Chrysostomou, and A. Gasteratos, “Obtaining reliable depth maps for robotic applications with a quad-camera system,” in International Conference on Intelligent Robotics and Applications, vol. 5928 of Lecture Notes in Computer Science, (Singapore), pp. 906–916, Springer-Verlag, December 2009.
  77. L. Nalpantidis, I. Kostavelis, and A. Gasteratos, “Stereovision-based algorithm for obstacle avoidance,” in International Conference on Intelligent Robotics and Applications, vol. 5928 of Lecture Notes in Computer Science, (Singapore), pp. 195–204, Springer-Verlag, December 2009.
  78. Y. Baudoin, D. Doroftei, G. De Cubber, S. A. Berrabah, C. Pinzon, F. Warlet, J. Gancet, E. Motard, M. Ilzkovitz, L. Nalpantidis, and A. Gasteratos, “View-Finder: Robotics assistance to fire-fighting services and crisis management,” in IEEE International Workshop on Safety, Security, and Rescue Robotics, (Denver, Colorado, USA), pp. 1–6, November 2009.
  79. I. Kostavelis, L. Nalpantidis, and A. Gasteratos, “Real-time algorithm for obstacle avoidance,” in Third Panhellenic Scientific Student Conference on Informatics, (Corfu, Greece), September 2009.
  80. L. Nalpantidis, A. Amanatiadis, G. C. Sirakoulis, N. Kyriakoulis, and A. Gasteratos, “Dense disparity estimation using a hierarchical matching technique from uncalibrated stereo vision,” in IEEE International Workshop on Imaging Systems and Techniques, (Shenzhen, China), pp. 427–431, May 2009.
  81. G. De Cubber, D. Doroftei, L. Nalpantidis, G. C. Sirakoulis, and A. Gasteratos, “Stereo-based terrain traversability analysis for robot navigation,” in IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance, (Brussels, Belgium), 2009.
  82. L. Nalpantidis, G. C. Sirakoulis, and A. Gasteratos, “A dense stereo correspondence algorithm for hardware implementation with enhanced disparity selection,” in 5th Hellenic Conference on Artificial Intelligence, vol. 5138 of Lecture Notes in Computer Science, (Syros, Greece), pp. 365–370, Springer-Verlag, 2008.
  83. G. De Cubber, L. Nalpantidis, G. C. Sirakoulis, and A. Gasteratos, “Intelligent robots need intelligent vision: Visual 3D perception,” in IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance, (Benicàssim, Spain), 2008.
  84. L. Nalpantidis, G. C. Sirakoulis, and A. Gasteratos, “Review of stereo matching algorithms for 3D vision,” in 16th International Symposium on Measurement and Control in Robotics, (Warsaw, Poland), pp. 116–124, 2007.
  85. I. Pappas, L. Nalpantidis, V. Kalenteridis, S. Siskos, A. A. Hatzopoulos, and C. A. Dimitriadis, “A threshold voltage variation cancellation technique for analogue peripheral circuits of a display array using poly-Si TFTs,” in IEEE International Symposium on Circuits and Systems, (Kos, Greece), May 2006.
  86. G. Fikos, L. Nalpantidis, and S. Siskos, “A low-voltage, analog power-law function generator,” in IEEE International Symposium on Circuits and Systems, (Kos, Greece), May 2006.
  87. I. Pappas, L. Nalpantidis, and S. Siskos, “A new analogue driver using poly-Si thin-film transistors for active matrix displays,” in XX Conference on Design of Circuits and Integrated Systems, (Lisboa, Portugal), November 2005.
  88. I. Pappas, L. Nalpantidis, V. Kalenteridis, S. Siskos, C. A. Dimitriadis, and A. A. Hatzopoulos, “A study of different types of current mirrors using polysilicon TFTs,” in Second Conference on Microelectronics, Microsystems and Nanotechnology, vol. 10 of Journal of Physics: Conference Series, (Athens, Greece), pp. 373–376, November 2005.
  89. A. A. Hatzopoulos, S. Siskos, C. A. Dimitriadis, N. Papadopoulos, I. Pappas, and L. Nalpantidis, “A built-in current sensor using thin-film transistors,” in Second Conference on Microelectronics, Microsystems and Nanotechnology, vol. 10 of Journal of Physics: Conference Series, (Athens, Greece), pp. 289–292, November 2005.
  90. L. Nalpantidis, G. Fikos, and S. Siskos, “A low-voltage, low-power generalized power-law function generator,” in XX Conference on Design of Circuits and Integrated Systems, (Lisboa, Portugal), November 2005.
  91. G. Fikos, L. Nalpantidis, and S. Siskos, “A 32x32 smart photo-array with minimum-size FGMOS for amplification and FPN reduction,” in IEEE Workshop on Signal Processing Systems Design and Implementation, (Athens, Greece), pp. 199–203, November 2005.

Books

  1. M. Vincze, T. Patten, H. I. Christensen, L. Nalpantidis, and M. Liu, eds., Computer Vision Systems, ICVS 2021, vol. 12899 of Lecture Notes in Computer Science. Springer, 2021.
  2. L. Nalpantidis, V. Krüger, J.-O. Eklundh, and A. Gasteratos, eds., Computer Vision Systems, ICVS 2015, vol. 9163 of Lecture Notes in Computer Science. Springer, 2015.

Editorials

  1. L. Nalpantidis, R. Detry, D. Damen, G. Bleser, M. Cakmak, and M. S. Erden, “Cognitive Robotics Systems: Concepts and Applications,” Guest Editorial, Journal of Intelligent and Robotic Systems, 2015.

Book Chapters

  1. L. Nalpantidis, “On the use of cellular automata in vision-based robot exploration,” in Robots and Lattice Automata (G. C. Sirakoulis and A. Adamatzky, eds.), vol. 13 of Emergence, Complexity and Computation, ch. 11, pp. 247–266, Springer, 2015.
  2. K. Charalampous, I. Kostavelis, E. Boukas, A. Amanatiadis, L. Nalpantidis, C. Emmanouilidis, and A. Gasteratos, “Autonomous robot path planning techniques using cellular automata,” in Robots and Lattice Automata (G. C. Sirakoulis and A. Adamatzky, eds.), vol. 13 of Emergence, Complexity and Computation, ch. 8, pp. 175–196, Springer, 2015.
  3. L. Nalpantidis, “Review of real-time stereo 3D imaging techniques,” in Interactive Displays: Natural Human-Interface Technologies (A. K. Bhowmik, ed.), ISBN: 978-1-118-63137-9, ch. 6, Wiley-Blackwell, 2014.
  4. I. Kostavelis, E. Boukas, L. Nalpantidis, and A. Gasteratos, “A mechatronic platform for robotic educational activities,” in Interdisciplinary Mechatronics: Engineering Science and Research Development (M. K. Habib and J. P. Davim, eds.), ISBN: 978-1-8482-1418-7, ch. 20, pp. 543–568, ISTE Wiley, 2013.
  5. L. Nalpantidis, I. Kostavelis, and A. Gasteratos, “Intelligent stereo vision in autonomous robot traversability estimation,” in Robotic Vision: Technologies for Machine Learning and Vision Applications (M. A. Cazorla Quevedo and J. Garcia-Rodriguez, eds.), IGI Global, 2012.
  6. L. Nalpantidis and A. Gasteratos, “Stereo vision depth estimation methods for robotic applications,” in Depth Map and 3D Imaging Applications: Algorithms and Technologies (A. S. Malik, T.-S. Choi, and H. Nisar, eds.), ISBN: 978-1-61350-326-3, ch. 21, pp. 397–417, IGI Global, 2011.

Theses

  1. L. Nalpantidis, “Study and implementation of stereo vision systems for robotic applications”, PhD thesis, Xanthi, 2010.
  2. L. Nalpantidis, “A design technique for power-law circuits”, Master thesis, Thessaloniki, 2005.
  3. L. Nalpantidis, “Study and design of a photosensitive analogue circuit array using Floating Gate MOS Transistors (FGMOS)”, Graduation thesis, Thessaloniki, 2003.

Technical Reports

  1. M. Kapoor, E. Katsanos, L. Nalpantidis, J. Winkler, and S. Thöns, “Structural health monitoring and management with unmanned aerial vehicles: Review and potentials,” Tech. Rep. BYG R-454, DTU Civil Engineering, 2021.