Everyone interested robotics, computer vision, and computer science in general is cordially invited to the School of Computer Science research seminar
on Friday, 10/11/2017 at 2pm
in room JUN0001 (The Junction).
Modelling and Detecting Objects for Home Robots
Markus Vincze, Technical University Vienna
In the near future service robots will start to handle objects in home tasks such as clearing the floor or table, making order or setting the table. Robots will need to know about all the objects in the environment. As a start, humans could show their favourite objects to the robot for obtaining full 3D models. These models are then used for object tracking and object recognition. Since modelling all objects in a home is cumbersome, learning object classes from the Web has become an option. While network based approaches do not perform too well in open settings, using 3D models and shape for detection in a hypothesis and verification scheme renders it possible to detect many objects touching each other. Finally, the models are linked to grasp point detection and warping, so that objects with small differences can be handled and the uncertainty of modelling as well as the robot grasping are taken care of. These methods are evaluated in settings for taking objects out of boxes, to pick up objects from the floor and for keeping track of objects in user homes.
Biography of Markus Vincze
Markus Vincze received his diploma in mechanical engineering from Technical University Wien (TUW) in 1988 and a M.Sc. from Rensselaer Polytechnic Institute, USA, 1990. He ﬁnished his PhD at TUW in 1993. With a grant from the Austrian Academy of Sciences he worked at HelpMate Robotics Inc. and at the Vision Laboratory of Gregory Hager at Yale University. In 2004, he obtained his habilitation in robotics. Presently he leads the “Vision for Robotics” team at TUW with the vision to make robots see. V4R regularly coordinates EU (e.g., ActIPret, robots@home, HOBBIT) and national research projects (e.g, vision@home) and contributes to research (e.g., CogX, STRANDS, Squirrel, ALOOF) and innovation projects (e.g., Redux, FloBot). With Gregory Hager he edited a book on Robust Vision for IEEE and is (co-)author of 42 peer reviewed journal articles and over 300 reviewed other publications. He was the program chair of ICRA 2013 in Karlsruhe and will organize HRI 2017 in Vienna. Markus’ special interests are cognitive computer vision techniques for robotics solutions situated in real-world environments and especially homes.
Making Robust SLAM Solvers for Autonomous Mobile Robots
WHERE: AAD1W11, Lecture Theatre (Art, Architecture and Design Building), Brayford Pool Campus
WHEN: Wednesday 24th May 2017, 3:00 – 4:00 pm
In robotics, simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it.
SLAM is an essential enabling technology for building truly autonomous robots that can operate in an unknown environment. The last three decades have seen substantial research in the field and modern SLAM systems are able to cope easily with operating conditions that in the past were regarded as challenging if not impossible to deal with.
This consideration might support the statement that SLAM is a closed problem. However a closer look at the contributions presented in the most relevant conferences and journals in robotics reveals that the papers on SLAM are still numerous and the community is large. Would this be the case if an off-the shelf solution that works all the time were available?
Non-experts that approach the problem, or even want to get one of the state-of-the-art systems running, often encounter problems and get performances that are far from the ones reported in the papers. This is usually because the person using the system is not the person designing the system. An open box approach that aims at solving the problems by modifying an existing pipeline is often hard to implement due to the complexity of modern SLAM systems.
In this talk we will overview the history of SLAM and we will outline some of the challenges in designing robust SLAM systems, and most importantly forming robust SLAM solvers.
Furthermore, we will also present PRO-SLAM (SLAM from a programmer’s perspective), a simplistic open-source pipeline that competes with state-of-the art Stereo Visual SLAM systems while focusing on simplicity to support teaching.
Caregiver 4.0 – Experiences from Introducing a Robot into a Geriatric Long Term Care Environment
Time: Monday, 11/7/16, 2pm
In my talk, I would like to give an overview of our scientific work that we conduct within the STRANDS-project, where the School of Computer Science of the University of Lincoln is also part of.
Due to demographic changes that lead to an ageing society, a shortage of care provision is anticipated. As a probable solution technical aids for enhancing independent living of older adults and for supporting staff in the elder care sector are proposed. But technical aids often lack required autonomy and were so far primarily tested in lab situations. Thus, the STRANDS –project came to live with the aim to develop a long-term autonomous learning robotic system that can be actually deployed in elder care and in other work environments under “real-world conditions” over longer periods of time.
Besides the technical challenges associated with such an endeavour, different questions were raised: What does staff in the elder care sector require from a robotic aid? In what areas could we deploy our STRANDS-robot in real world conditions? How would older adults and care staff experience interacting or working with the robot? What ethical guidelines have to be met when introducing a robotic aid in such an environment? And what could the future with such robotic aids look like in elder care? Questions that will be addressed in this presentation.
Denise Hebesberger studied Biology (grad. 2013) and Educational Science (grad. 2012) at the University of Vienna. After graduation and working in different fields of science, she joined the Academy for Research on Ageing as a project manager in 2014. The Academy is social science partner within different EU-wide research consortia that develop technical aids and assistive systems for older adults or for the care sector and study their impact in terms of social acceptance and human-robot interaction on end users. She is responsible for establishing theoretical frameworks, evaluation designs and data analysis (mixed methods designs & structural equation modelling), as well as dissemination of research results and scientific publications.
After Dr Cuayahuitl and Dr Baxter, who gave research presentations recently, we are now happy to announce a research seminar by the third colleague to join the Lincoln Centre for Autonomous Systems soon as a Senior Lecturer.
On 15/02/16, at 2pm, in room MB1020 (1st floor, Minerva Building), Dr Michael Mangan, currently still at the University of Edinburgh, will be presenting his exciting research. Everybody is invited to join in.
What can self-driving cars learn from the humble desert ant? And how are those lessons learned?
Desert ants are amongst the most impressive of the animal navigators: expertly piloting through complex environments despite possessing low-resolution eyes and tiny brains. As such they are an ideal model system for bio-roboticists that seek to understand these amazing animals, as well as those seeking novel solutions for engineering goals such as autonomous navigation. In this talk I shall firstly introduce the animal of interest (the desert ant) describing their amazing navigational capabilities. I will then briefly describe some recent examples for which our bio-robotic approach has lead to advances in understanding of the biological system and novel applications in autonomous systems (such as self-driving cars). I shall close by looking ahead to the research I shall be pursuing after joining the University of Lincoln this spring.