Research Project

LOGIMATIC: Tight integration of EGNSS and on-board sensors for port vehicle automation

Type

European Project

Start Date

01/03/2016

End Date

31/08/2019

Project Code

H2020-Galileo-2015-1-687534

Project illustration

Staff

Project Description

Annual volume at global container terminals will rise by a 5.6% rate during the next 5 years and reach 840 million TEU by 2018.The usual way for ports to deal with the increasing demand of sea transport and compete against competitors is to expand the port in the original site. There is scarcity of land available for port expansion in densely populated urban areas where most of European ports are situated. This fact is causing that many container terminals are coping with congestion and capacity problems. Therefore, port managers are searching for more efficient and cost-effective means in the handling of containers while still trying to introduce innovative technical solutions. Container handling equipment automation is an innovative technological solution that contributes not only to improve the utilization rate of equipment and reduce operating costs, but also greatly improve efficiency of terminals.
LOGIMATIC proposes an ad-hoc advanced location and navigation solution to enable the automation of existing port vehicles with a significantly lower cost which will allow short-medium term investments until the whole port fleet is renewed with totally autonomous vehicles in the long term. The project will develop and demonstrate an innovative location and navigation solution for the automation of the operations of straddle carriers in container terminals.
Objectives:
● To develop an advanced automated navigation solution based on the integration of Global Navigation Satellite Systems (GNSS) and sensors onboard the SC vehicles.
● To implement a GIS-based control module compatible with existing Terminal Operating Systems (TOS) for optimized global (yard level) route planning and fleet management.
● To implement security mechanism in order to detect and avoid spoofing and/or jamming attacks
● To assess the impact of application of such automated approach at large scale through simulation
● To integrate, validate and demonstrate the proposed solution in a real port yard.






More info: https://logimatic-project.eu/

Project Publications

Journal Publications

  • J. Solà, J. Vallvé, J. Casals, J. Deray, M. Fourmy, D. Atchuthan, A. Corominas Murtra and J. Andrade-Cetto. WOLF: A modular estimation framework for robotics based on factor graphs. IEEE Robotics and Automation Letters, 7(2): 4710-4717, 2022.

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  • V. Vaquero, I. del Pino, F. Moreno-Noguer, J. Solà, A. Sanfeliu and J. Andrade-Cetto. Dual-branch CNNs for vehicle detection and tracking on LiDAR data. IEEE Transactions on Intelligent Transportation Systems, 22(11): 6942-6953, 2021.

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  • J. Vallvé, J. Solà and J. Andrade-Cetto. Pose-graph SLAM sparsification using factor descent. Robotics and Autonomous Systems, 119: 108-118, 2019.

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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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  • 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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  • J. Deray, J. Solà and J. Andrade-Cetto. Word ordering and document adjacency for large loop closure detection in 2D laser maps. IEEE Robotics and Automation Letters, 2(3): 1532-1539, 2017.

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Conference Publications

  • J. Deray, B. Magyar, J. Solà and J. Andrade-Cetto. Timed-elastic smooth curve optimization for mobile-base planning, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019, Macau, China, pp. 3143-3149.

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  • J. Deray, J. Solà and J. Andrade-Cetto. Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration, 9th European Conference on Mobile Robots, 2019, Prague, pp. 1-6.

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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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  • I. del Pino, V. Vaquero, B. Masini, J. Solà, F. Moreno-Noguer, A. Sanfeliu and J. Andrade-Cetto. Low resolution lidar-based multi object tracking for driving applications, 3rd Iberian Robotics Conference, 2017, Seville, in Robot 2017: Third Iberian Robotics Conference, Vol 694 of Advances in Intelligent Systems and Computing, pp. 287-298, Springer.

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  • A. Agudo and F. Moreno-Noguer. Global model with local interpretation for dynamic shape reconstruction, 17th IEEE Winter Conference on Applications of Computer Vision, 2017, Santa Rosa, CA, USA, pp. 264-272, IEEE.

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  • A. Agudo and F. Moreno-Noguer. DUST: Dual union of spatio-temporal subspaces for monocular multiple object 3D reconstruction, 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017, Honolulu, HW, USA, pp. 1513-1521, IEEE.

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  • V. Vaquero, I. del Pino, F. Moreno-Noguer, J. Solà, A. Sanfeliu and J. Andrade-Cetto. Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios, 8th European Conference on Mobile Robots, 2017, Paris, France, pp. 1-7.

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  • J. Vallvé, J. Solà and J. Andrade-Cetto. Factor descent optimization for sparsification in graph SLAM, 8th European Conference on Mobile Robots, 2017, Paris, France, pp. 1-6.

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