2020

  1. Rômulo T. Rodrigues, Pedro Miraldo, Dimos V. Dimarogonas, A. Pedro Aguiar (2020),
    Active Depth Estimation: Stability Analysis and its Applications,
    IEEE Int'l Conf. Robotics and Automation (ICRA), [doi];

2019

  1. P. U. Lima, C. Azevedo, E. Brzozowska, J. Cartucho, T. J. Dias, J. Gonçalves, M. Kinarullathil,
    G. Lawless, O. Lima, R. Luz, P. Miraldo, E. Piazza, M. Silva, T. Veiga, and R. Ventura (2019),
    SocRob@Home: Integrating AI Components in a Domestic Robot System,
    Künstliche Intelligenz (KI), pp. 1-14 [doi];
  2. R. T. Rodrigues, Pedro Miraldo, D. V. Dimarogonas, A. P. Aguiar (2019),
    A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), [arXiv:1908.00309,doi];
  3. P. Miraldo, S. Saha, and S. Ramalingam (2019),
    Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp: 9691-9700 [arXiv:1904.03941,doi];
  4. A. Mateus, O. Tahri, and P. Miraldo (2019),
    Active Estimation of 3D Lines in Spherical Coordinates,
    American Control Conference (ACC), pp: 3950-3955 [arXiv:1902.00473,doi];
  5. G. Pais, T. J. Dias, J. Nascimento, and P. Miraldo (2019),
    OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp: 4782-4789 [arXiv:1903.00676,doi];
  6. J. Campos, J. R. Cardoso, and P. Miraldo (2019),
    POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 3493-3499 [arXiv:1904.04858,code,doi];
  7. A. Mateus, D. Ribeiro, P. Miraldo, and J. C. Nascimento (2019),
    Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation,
    Robotics and Autonomous Systems Journal (RAS), 113:23-37, [arXiv:1607.04441,doi];

2018

  1. P. Miraldo, T. Dias, S. Ramalingam (2018),
    A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines,
    European Conf. Computer Vision (ECCV), pp: 490-507 [arXiv:1807.09970,project,doi];
  2. A. Mateus, O. Tahri, and P. Miraldo (2018),
    Active Structure-from-Motion for 3D Straight Lines,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp: 5819-5825 [arXiv:1807.00753,doi];
  3. P. Miraldo, F. Eiras, and S. Ramalingam (2018),
    Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp: 2012-2021 [arXiv:1804.09460,project,doi];
  4. R. T. Rodrigues, M. Basiri, A. P. Aguiar, and P. Miraldo (2018),
    Low-level Active Visual Navigation:
    Increasing robustness of vision-based localization using potential fields
    ,
    IEEE Robotics and Automation Letters (RA-L), and IEEE Int'l Conf. Robotics and Automation (ICRA), 3(3):2079-2086 [arXiv:1801.07249,doi];
  5. X. Liu, Z. Li, K. Zhong, Y. Chao, P. Miraldo, and Y. Shi (2018),
    Generic distortion model for metrology under optical microscopes,
    Optics and Lasers in Engineering (OLEN) 103:119-126 [doi];

2017

  1. L. Iocchi, G. Kraetzschmar, D. Nardi, P. U. Lima, P. Miraldo, E. Bastianelli, and R. Capobianco (2017),
    RoCKIn@Home: Domestic Robots Challenge,
    RoCKIn - Benchmarking Through Robot Competitions, IntechOpen [doi];
  2. R. Rodrigues, M. Basiri, A. P. Aguiar, and P. Miraldo (2017),
    Feature Based Potential Field for Low-level Active Visual Navigation,
    Iberian Robotics Conf. (ROBOT), pp: 791-800 [arXiv:1709.04687,doi];
  3. D. Ribeiro, A. Mateus, P. Miraldo, and J. C. Nascimento (2017),
    A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation,
    IEEE Int'l Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp: 165-171 [arXiv:1607.04436,doi];

2016

  1. J. Cardoso, P. Miraldo, and H. Araujo (2016),
    Plcker correction problem: Analysis and improvements in efficiency,
    IEEE/IAPR Int'l Conf. on Pattern Recognition (ICPR), pp: 2796-2801 [pdf,doi];
  2. J. Iglsias, P. Miraldo, and R. Ventura (2016),
    Towards an Omnidirectional Catadioptric RGB-D Camera,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp: 2506-2513 [pdf,doi];
  3. T. Veiga, P. Miraldo, R. Ventura, and P. Lima (2016),
    Efficient Object Search for Mobile Robots in Dynamic Environments:
    Semantic Map as an Input for the Decision Maker
    ,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp: 2745-2750 [pdf,doi];
  4. R. Ventura, M. Basiri, A. Mateus, J. Garcia, P. Miraldo, P. Santos, and P. U. Lima (2016),
    A Domestic Assistive Robot Developed Through Robotic Competitions,
    WS Autonomous Mobile Service Robots joint with
    Int'l Joint Conference on Artificial Intelligence (IJCAI) [pdf];
  5. T. Dias, H. Araujo, and P. Miraldo (2016),
    3D Reconstruction with Low-Resolution, High Radial Distortion Stereo Images,
    ACM Int'l Conf. on Distributed Smart Cameras (ICDSC), pp: 98-103 [pdf,doi];
  6. T. Dias, P. Miraldo, and N. Gonçalves (2016),
    Augmented Reality on Non-Central Catadioptric Camera Systems,
    Journal of Intelligent & Robotic Systems (JINT), 83(3):359-373 [pdf,doi];
  7. X. Liu, Z. Li, P. Miraldo, K. Zhong, and Y. Shi (2016),
    A Framework to Calibrate the Scanning Electron Microscope under Variational Magnifications,
    IEEE Photonics Technology Letters, 28(16):1715-1718 [doi];

2015

  1. F. Amigoni, E. Bastianelli, J. Berghofer, A. Bonarini, G. Fontana, N. Hochgeschwender,
    L. Iocchi, G. K. Kraetzschmar, P. Lima, M. Matteucci, P. Miraldo, D. Nardi, V. Schiaffonati (2015),
    Enabling Replicable Experiments and Benchmarking with RoCKIn Competitions,
    IEEE Robotics ans Automation Magazine (RAM), 22(3):53-61 [doi];
  2. A. Mateus, P. Miraldo, P. Lima, and J. Sequeira (2015),
    Human-Aware Navigation using External Omnidirectional Cameras,
    Iberian Robotics Conf. (ROBOT), pp: 283-295 [pdf,doi];
  3. T. Dias, P. Miraldo, N. Gonalves, and P. Lima (2015),
    Augmented Reality on Robot Navigation using Non-Central Catadioptric Cameras,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp: 4999-5004 [pdf,doi];
  4. P. Miraldo and H. Araujo (2015),
    Pose Estimation for Non-Central Cameras Using Planes,
    Journal of Intelligent & Robotic Systems (JINT), 80(3):595-608 [pdf,doi]
  5. T. Dias, P. Miraldo, and N. Gonalves (2015),
    A Framework for Augmented Reality using Non-Central Catadioptric Cameras,
    IEEE Int'l Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp: 213-220 [pdf,doi];
  6. P. Miraldo and H. Araujo (2015),
    Generalized Essential Matrix: Properties of the Singular Value Decomposition,
    Image and Vision Computing (IVC), 34:45-50 [pdf,doi];
  7. P. Miraldo, H. Araujo and N. Gonçalves (2015),
    Pose Estimation for General Cameras using Lines,
    IEEE Trans. Cybermetic (Systems, Man, and Cybernetics, Part B), 45(10):2156-2164 [pdf,doi];

2014

  1. P. Miraldo and H. Araujo (2014),
    Direct Solution to the Minimal Generalized Pose,
    IEEE Trans. Cybermetic (Systems, Man, and Cybernetics, Part B), 45(3):404-415 [pdf,doi];
  2. P. Miraldo and H. Araujo (2014),
    Planar Pose Estimation for General Cameras using Known 3D Lines,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp: 4234-4240 [pdf,doi];
  3. P. Miraldo and H. Araujo (2014),
    Pose Estimation for Non-Central Cameras Using Planes,
    IEEE Int'l Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp: 104-109 [pdf,doi];
  4. P. Miraldo and H. Araujo (2014),
    A Simple and Robust Solution to the Minimal General Pose Estimation,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp: 2119-2125 [pdf,doi];

Before 2014

  1. P. Miraldo and H. Araujo (2013),
    Calibration of Smooth Camera Models,
    IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI), 35(9):2091-2103 [pdf,appendix,doi];
  2. P. Miraldo, H. Araujo, and J. Queiro (2011),
    Point-based Calibration Using a Parametric Representation of General Imaging Models,
    IEEE Int'l Conf. Computer Vision (ICCV), pp: 2304-2311 [pdf,appendix, doi];
  3. P. Miraldo and H. Araujo (2010)
    Improving the Resolution of the Generic Camera Model by Means of a Parametric Representation,
    Portuguese Conf. Automatic Control (CONTROLO);
  4. P. Miraldo and H. Araujo (2008),
    Gestures Interpretation Using Computer Vision for Human-Machine Interaction,
    Portuguese Conf. Pattern Recognition (RECPAD);

Thesis

  1. P. Miraldo (2013). General Camera Models: Calibration and Pose,
    PhD in Electrical Engineering, University of Coimbra [pdf];
  2. P. Miraldo (2008), "Interpretação de Gestos Usando Visão por Computador para Interacção Homem Máquina"
    MSc in Electrical and Computer Engineering, University of Coimbra.