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OpenDR: Open Deep Learning Toolkit for Robotics

01/01/2020 → 31/12/2022

PI in AU: Alexandros Iosifidis

coPI in AU: Erdal Kayacan

Programme: HORIZON 2020 - LEIT ICT WORK PROGRAMME 2018-2020

The core objective of OpenDR is to provide real-time (as defined by the specific use case requirements), light-weight (to be deployed in use-case specified embedded CPUs/GPUs like the NVIDIA TX1/2 and Xavier) and ultra light-weight (to be deployed in embedded architectures like Raspberry Pi, FPGAs, microcontrollers, etc.) deep learning architectures for performing inference and control in various robotics tasks.

AU Role: AU will lead a task on 2D/3D Object localization and tracking and will work on sensor information fusion, as well as contributing to object detection/recognition and semantic scene segmentation and understanding.  AU will also contribute on a work package by working on deep person/face/body part active detection/recognition and pose estimation, deep person/face/body part tracking, human activity recognition, social signal analysis and recognition and multi-modal human centric perception and cognition. AU will also work on another work package on deep planning, deep navigation and deep action and control.

Reliable AI for Marine Robotics (ReMaRo)

01/12/2020 → 1/12/2024

PI: Erdal Kayacan

Programme: HORIZON 2020 - H2020-MSCA-ITN-2020, European Union

Smart Parking System for Vessels and Ports

01/06/2020 → 01/04/2021

PI: Erdal Kayacan

Programme: European Regional Development Fund

The aim of this project, in a collaboration with a start up company in Denmark, is to measure the distance between the harbour and the vessel when parking in the harbour using aerial robots.

Vision-based inspection navigation algorithm for ship inspection

01/08/2020 → 01/08/2021

PI: Erdal Kayacan

Programme: European Regional Development Fund

The aim of this project, in a collaboration with a start up company in Denmark, is the autonomous inspection vessels using visual SLAM algorithms in the harbour using aerial robots.

Finished Projects

Visualization of Virtual Outcrops using Aerial Robots

01/03/2019 → 31/12/2019

Project Coordinator: Erdal Kayacan

In order to generate 3D virtual maps of outcrops in geoscience, a manual flight of aerial robots is often employed which is challenging due to various reasons: 1) piloted flight over curved/uneven surfaces requires auto-focusing, 2) wind disturbances make it difficult even for skilled pilots to precisely main- tain the desired overlap, and 3) hiring of a skilled pilot is expensive as the outcrop generation requires hours of visualization data. In this work, we propose to fully automate the visualization process using a learning-based control framework, i.e., position tracking nonlinear model predictive controller in conjunction with Gaussian process (GP)-based disturbance regression which facilitates a precise tracking of the generated path. Thanks to the long-short term memory feature of the designed GP model, the disturbance forces are accurately estimated even for increasing magnitude levels and time-periods. The simulation and real-world tests manifest that the proposed method could provide a time- and cost-saving yet reliable visualization framework.