Research line

Mobile Robotics and Intelligent Systems Image

The research activities of the MOBILE ROBOTICS line are aimed to endow mobile robots and ubiquitous computing devices the necessary skills to aid humans in everyday life activities. These skills range from pure perceptual activities such as tracking, recognition or situation awareness, to motion skills, such as localization, mapping, autonomous navigation, path planning or exploration.

Head of line: Alberto Sanfeliu Cortés

Head of line

Tech. transfer

Our activity finds applications in several fields through collaboration with our technological partners

Research projects

We carry out projects from national and international research programmes.
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Urban service robotics

The group focuses on the design and development of service mobile robots for human assistance and human robot interaction. This includes research on novel hardware and software solutions to urban robotic services such as surveillance, exploration, cleaning, transportation, human tracking, human assistance and human guiding.

Research area 1 of Mobile Robotics

Social robotics

The group's work on social robotics has an emphasis in human robot interaction and collaboration, developing new techniques to predict and learn human behaviors, human-robot task collaboration, and the generation of emphatic robot behaviors using all types of sensors, computer vision techniques and cognitive systems technologies.

Research area 2 of Mobile Robotics

Robot localization and robot navigation

This research area tackles the creation of robust single and cooperative, indoor and outdoor robot localization solutions, using multiple sensor modalities such as GPS, computer vision and laser range finding, INS sensors and raw odometry. The area also seeks methods and algorithms for autonomous robot navigation, and robot formation; and the application of these methods on a variety of indoor and outdoor mobile robot platforms.

Research area 3 of Mobile Robotics

SLAM and robot exploration

We develop solutions for indoor and outdoor simultaneous localization and mapping using computer vision and three-dimensional range data using Bayesian estimation. The research includes the development of new filtering and smoothing algorithms that limit the load of maps using information theoretic measures; as well as the design and construction of novel sensors for outdoor mapping. This research area also studies methods for autonomous robotic exploration.

Research area 4 of Mobile Robotics

Tracking in computer vision

We study the development of robust algorithms for the detection and tracking of human activities in indoor and outdoor areas, with applications to service robotics, surveillance, and human-robot interaction. This includes the development of fixed/moving single camera tracking algorithms as well as detection and tracking methods over large camera sensor networks.

Research area 5 of Mobile Robotics

Object recognition

The group also performs research on object detection and object recognition in computer vision. Current research is heavily based on boosting and other machine learning methodologies that make extensive use of multiple view geometry. We also study the development of unique feature and scene descriptors, invariant to changes in illumination, cast shadows, or deformations.

Research area 6 of Mobile Robotics

These are the latest research projects of the Mobile Robotics and Intelligent Systems research line:

These are the most recent publications (2016 - 2015) of the Mobile Robotics and Intelligent Systems

  • E. Trulls Fortuny, I. Kokkinos, A. Sanfeliu and F. Moreno-Noguer. Dense segmentation-aware descriptors. In Dense Image Correspondences for Computer Vision, 83-107. Springer, 2016.

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  • G. Sanromà, A. Penate-Sanchez, R. Alquézar Mancho, F. Serratosa, F. Moreno-Noguer, J. Andrade-Cetto and M.A. González. MSClique: Multiple structure discovery through the maximum weighted clique problem. PLOS One, 11(1): e0145846, 2016.

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  • V. Lippiello, J. Cacace, A. Santamaria-Navarro, J. Andrade-Cetto, M.A. Trujillo, Y. Rodriguez and A. Viguria. Hybrid visual servoing with hierarchical task composition for aerial manipulation. IEEE Robotics and Automation Letters, 1(1): 259-266, 2016.

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  • M. Igelmo and A. Sanfeliu. Filtering graphs to check isomorphism and extracting mapping by using the conductance electrical model. Pattern Recognition, 2016, to appear.

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  • A. Corominas Murtra, J. Vallvé, J. Solà, I. Flores and J. Andrade-Cetto. Observability analysis and optimal sensor placement in stereo radar odometry, 2016 IEEE International Conference on Robotics and Automation, 2016, Stockholm, Sweden, pp. 3161-3166.

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  • J. Solà. Quaternion kinematics for the error-state Kalman filter. Technical Report IRI-TR-16-02, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2016.

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  • A. Amor, A. Santamaria-Navarro, F. Herrero, A. Ruiz and A. Sanfeliu. Planar P∅P: feature-less pose estimation with applications in UAV localization, 2016 IEEE International Symposium on Safety, Security and Rescue Robotics, 2016, Lausanne, to appear.

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  • I. Alzugaray and A. Sanfeliu. Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016, Daejeon, Korea, to appear.

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  • M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Random clustering ferns for multimodal object recognition. Neural Computing and Applications, 2016, to appear.

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  • M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Interactive multiple object learning with scanty human supervision. Computer Vision and Image Understanding, 149: 51-64, 2016.

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  • A. Goldhoorn, A. Garrell Zulueta, R. Alquézar Mancho and A. Sanfeliu. Un nuevo método cooperativo para encontrar personas en un entorno urbano con robots móviles, XXXVII Jornadas de Automática, 2016, Madrid, pp. 206-213.

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  • J. Andrade-Cetto, U. Frese and M. Tenorth. ECMR’13 Special Issue. Robotics and Autonomous Systems, 69: 1-2, 2015.

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  • A. Corominas Murtra, S. Pfeiffer and J. Pagès. Multi-target & multi-detector people tracker for mobile robots, 2015 European Conference on Mobile Robots, 2015, Lincoln, UK, pp. 1-6.

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  • S. Hernández and F. Herrero. Multi-master ROS systems. Technical Report IRI-TR-15-01, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2015.

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  • J. Vallvé and J. Andrade-Cetto. Active Pose SLAM with RRT*, 2015 IEEE International Conference on Robotics and Automation, 2015, Seattle, pp. 2167-2173.

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  • A. Rubio, M. Villamizar, L. Ferraz, A. Penate-Sanchez, A. Ramisa, E. Simo-Serra, A. Sanfeliu and F. Moreno-Noguer. Efficient monocular pose estimation for complex 3D models, 2015 IEEE International Conference on Robotics and Automation, 2015, Seattle, pp. 1397-1402.

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  • M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Modeling robot's world with minimal effort, 2015 IEEE International Conference on Robotics and Automation, 2015, Seattle, pp. 4890-4896.

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  • M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Multimodal object recognition using random clustering trees, 7th Iberian Conference on Pattern Recognition and Image Analysis, 2015, Santiago de Compostela, in Pattern Recognition and Image Analysis, Vol 9117 of Lecture Notes in Computer Science, pp. 496-504, 2015, Springer.

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  • S. Hernández and F. Herrero. Autonomous navigation framework for a car-like robot. Technical Report IRI-TR-15-07, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2015.

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  • G. Ferrer and A. Sanfeliu. Multi-objective cost-to-go functions on robot navigation in dynamic environments, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015, Hamburg, pp. 3824-3829.

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  • A. Santamaria-Navarro, J. Solà and J. Andrade-Cetto. High-frequency MAV state estimation using low-cost inertial and optical flow measurement units, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015, Hamburg, pp. 1864-1871.

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  • R. Galego, A.A. Ortega, R. Ferreira, A.J. Bernardino Malheiro, J. Andrade-Cetto and J. Gaspar. Uncertainty analysis of the DLT-lines calibration algorithm for cameras with radial distortion. Computer Vision and Image Understanding, 140: 115-126, 2015.

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  • A. Penate-Sanchez, L. Porzi and F. Moreno-Noguer. Matchability prediction for full-search template matching algorithms, 2015 International Conference on 3D Vision, 2015, Lyon, pp. 353-361.

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  • V. Vaquero, M. Villamizar and A. Sanfeliu. Real time people detection combining appearance and depth image spaces using boosted random ferns, 2nd Iberian Robotics Conference, 2015, Lisbon, in Robot 2015: Second Iberian Robotics Conference, Vol 418 of Advances in Intelligent Systems and Computing, pp. 587–598, 2015.

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  • V. Vaquero, E. Repiso, A. Sanfeliu, J. Vissers and M. Kwakkernaat. Low cost, robust and real time system for detecting and tracking moving objects to automate cargo handling in port terminals, 2nd Iberian Robotics Conference, 2015, Lisbon, in Robot 2015: Second Iberian Robotics Conference, Vol 418 of Advances in Intelligent Systems and Computing, pp. 491–502, 2015.

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  • A. Santamaria-Navarro, E.H. Teniente, M. Morta and J. Andrade-Cetto. Terrain classification in complex three-dimensional outdoor environments. Journal of Field Robotics, 32(1): 42-60, 2015.

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  • D. Escudero and R. Alquézar Mancho. Distance-based kernels for dynamical movement primitives, 18th Catalan Conference on Artificial Intelligence, 2015, Valencia, in Artificial Intelligence Research and Development, Vol 277 of Frontiers in Artificial Intelligence and Applications, pp. 133-142, 2015, IOS Press.

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  • J. Vallvé and J. Andrade-Cetto. Potential information fields for mobile robot exploration. Robotics and Autonomous Systems, 69: 68-79, 2015.

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  • I. Huerta, M.B. Holte, T. Moeslund and J. Gonzàlez. Chromatic shadow detection and tracking for moving foreground segmentation. Image and Vision Computing, 41: 42–53, 2015.

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  • I. Huerta, M. Pedersoli, J. Gonzàlez and A. Sanfeliu. Combining where and what in change detection for unsupervised foreground learning in surveillance. Pattern Recognition, 48(3): 709-719, 2015.

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  • C. König, M.I. Cárdenas, J. Giraldo, R. Alquézar Mancho and A. Vellido. Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors. BMC Bioinformatics, 16(314): 1-14, 2015.

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Mobile Robotics Laboratory

The Mobile Robotics Laboratory is an experimental area primarily devoted to hands-on research with mobile robot devices. The lab includes 3 Pioneer platforms, 2 service robots for urban robotics research based on Segway platforms, and a 4-wheel rough outdoor mobile robot, a six-legged LAURON-III walking robot, and a vast number of sensors and cameras.

Mobile Robotics Laboratory

Barcelona Robot Laboratory

The Barcelona Robot Lab encompasses an outdoor pedestrian area of 10.000 sq m., and is provided with 21 fixed cameras, a set of heterogeneous robots, full coverage of wifi and mica devices, and partial gps coverage. The area has moderate vegetation and intense cast shadows, making computer vision algorithms more than challenging.

Barcelona Robot Laboratory
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