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 (2019 - 2018) of the Mobile Robotics and Intelligent Systems

  • A. Penate-Sanchez. Camera Pose Estimation in Complex Environments. LAP Lambert, 2019.

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  • A. Santamaria-Navarro, J. Solà and J. Andrade-Cetto. Visual Guidance of Unmanned Aerial Manipulators. Volume 125 of Springer Tracts in Advanced Robotics. Springer International Publishing, 2019.

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  • V. Vaquero, E. Repiso and A. Sanfeliu. Robust and real-time detection and tracking of moving objects with minimum 2D LiDAR information to advance autonomous cargo handling in ports. Sensors, 19(1): 107, 2019.

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  • M. Villamizar, A. Sanfeliu and F. Moreno-Noguer. Online learning and detection of faces with low human supervision. The Visual Computer, 35(3): 349-370, 2019.

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  • J. Pérez and A. Sanfeliu. Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving. Robotics and Autonomous Systems, 114: 93-105, 2019.

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  • A. Pumarola, A. Agudo, L. Porzi, A. Sanfeliu, V. Lepetit and F. Moreno-Noguer. Geometry-aware network for non-rigid shape prediction from a single view, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, pp. 4681-4690.

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  • A. Pumarola, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. Unsupervised person image synthesis in arbitrary poses, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, pp. 8620-8628.

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  • A. Pumarola, A. Agudo, A.M. Martinez, A. Sanfeliu and F. Moreno-Noguer. GANimation: Anatomically-aware facial animation from a single image, 15th European Conference on Computer Vision, 2018, Munich, in Computer Vision - ECCV 2018, Vol 11214 of Lecture Notes in Computer Science, pp. 835-851, 2018.

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  • A. Ollero, G. Heredia, A. Franchi, G. Antonelli, K. Kondak, A. Sanfeliu, A. Viguria, J.R. Martínez de Dios, F. Pierri, J. Cortés, A. Santamaria-Navarro, M.A. Trujillo, R. Balachandran, J. Andrade-Cetto and A. Rodríguez. The AEROARMS project: Aerial robots with advanced manipulation capabilities for inspection and maintenance. IEEE Robotics and Automation Magazine, 25(4): 12-23, 2018.

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  • V. Vaquero, A. Sanfeliu and F. Moreno-Noguer. Hallucinating dense optical flow from sparse lidar for autonomous vehicles, 24th International Conference on Pattern Recognition, 2018, Beijing, China, pp. 1959-1964.

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  • J. Solà, J. Deray and D. Atchuthan. A micro Lie theory for state estimation in robotics. Technical Report IRI-TR-18-01, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2018.

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  • E. Repiso, A. Garrell Zulueta and A. Sanfeliu. Robot approaching and engaging people in a human-robot companion framework, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, Madrid, pp. 8200-8205.

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  • A. Grau, Y. Bolea, A.M. Puig-Pey, A. Sanfeliu and J. Casanovas. Sustainable robotics solutions in smart cities. The challenge of the ECHORD++ project, 23rd IEEE International Conference on Emerging Technologies and Factory Automation, 2018, Torino, Italy, pp. 1291-1296.

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  • D. Escudero and R. Alquézar Mancho. Considering the anchoring problem in robotic intelligent bin picking, 21st Catalan Conference on Artificial Intelligence, 2018, Roses, in Artificial Intelligence Research and Development, Vol 308 of Frontiers in Artificial Intelligence and Applications, pp. 350-354, 2018, IOS Press.

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  • A. Grau, Y. Bolea, A. Sanfeliu and A.M. Puig-Pey. An innovative ICT solution for sewer systems, 13th International Conference on Hydroinformatics, 2018, Palermo, Italy, Vol 3 of EpIC Series in Engineering, pp. 852-857.

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  • M. Villamizar, J. Andrade-Cetto, A. Sanfeliu and F. Moreno-Noguer. Boosted random ferns for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2): 272-288, 2018.

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  • A. Grau, Y. Bolea, A. Sanfeliu and A.M. Puig-Pey. The city of the future: The challenge of the ECHORD++ Project, 6th International Multidisciplinary Conference, 2018, Lisbon, pp. 125-145.

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  • R. Valencia and J. Andrade-Cetto. Mapping, Planning and Exploration with Pose SLAM. Volume 119 of Springer Tracts in Advanced Robotics. Springer, 2018.

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  • A. Grau, E. Guerra, Y. Bolea, A.M. Puig-Pey and A. Sanfeliu. Aerial robotics in building inspection and maintenance, 2018 International Conference on Smart, Sustainable and Sensuous Settlements Transformation, 2018, Munich, Germany, pp. 193-198.

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  • A. Santamaria-Navarro, G. Loianno, J. Solà, V. Kumar and J. Andrade-Cetto. Autonomous navigation of micro aerial vehicles: State estimation using fast and low-cost sensors. Autonomous Robots, 42(6): 1263-1280, 2018.

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  • J. Vallvé, J. Solà and J. Andrade-Cetto. Graph SLAM sparsification with populated topologies using factor descent optimization. IEEE Robotics and Automation Letters, 3(2): 1322-1329, 2018.

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  • A. Goldhoorn, A. Garrell Zulueta, R. Alquézar Mancho and A. Sanfeliu. Searching and tracking people with cooperative mobile robots. Autonomous Robots, 42(4): 739-759, 2018.

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  • V. Vaquero, A. Sanfeliu and F. Moreno-Noguer. Deep lidar CNN to understand the dynamics of moving vehicles, 2018 IEEE International Conference on Robotics and Automation, 2018, Brisbane, Australia, pp. 4504-4509.

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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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