3,153 research outputs found
2D Visual Place Recognition for Domestic Service Robots at Night
Domestic service robots such as lawn mowing and vacuum cleaning robots are
the most numerous consumer robots in existence today. While early versions
employed random exploration, recent systems fielded by most of the major
manufacturers have utilized range-based and visual sensors and user-placed
beacons to enable robots to map and localize. However, active range and visual
sensing solutions have the disadvantages of being intrusive, expensive, or only
providing a 1D scan of the environment, while the requirement for beacon
placement imposes other practical limitations. In this paper we present a
passive and potentially cheap vision-based solution to 2D localization at night
that combines easily obtainable day-time maps with low resolution
contrast-normalized image matching algorithms, image sequence-based matching in
two-dimensions, place match interpolation and recent advances in conventional
low light camera technology. In a range of experiments over a domestic lawn and
in a lounge room, we demonstrate that the proposed approach enables 2D
localization at night, and analyse the effect on performance of varying
odometry noise levels, place match interpolation and sequence matching length.
Finally we benchmark the new low light camera technology and show how it can
enable robust place recognition even in an environment lit only by a moonless
sky, raising the tantalizing possibility of being able to apply all
conventional vision algorithms, even in the darkest of nights
Robust Place Categorization With Deep Domain Generalization
Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases, this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper, we present an approach that aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g., corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a convolutional neural network architecture with novel layers performing a weighted version of batch normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution
CHARMIE: a collaborative healthcare and home service and assistant robot for elderly care
The global population is ageing at an unprecedented rate. With changes in life expectancy across the world, three major issues arise: an increasing proportion of senior citizens; cognitive and physical problems progressively affecting the elderly; and a growing number of single-person households. The available data proves the ever-increasing necessity for efficient elderly care solutions such as healthcare service and assistive robots. Additionally, such robotic solutions provide safe healthcare assistance in public health emergencies such as the SARS-CoV-2 virus (COVID-19). CHARMIE is an anthropomorphic collaborative healthcare and domestic assistant robot capable of performing generic service tasks in non-standardised healthcare and domestic environment settings. The combination of its hardware and software solutions demonstrates map building and self-localisation, safe navigation through dynamic obstacle detection and avoidance, different human-robot interaction systems, speech and hearing, pose/gesture estimation and household object manipulation. Moreover, CHARMIE performs end-to-end chores in nursing homes, domestic houses, and healthcare facilities. Some examples of these chores are to help users transport items, fall detection, tidying up rooms, user following, and set up a table. The robot can perform a wide range of chores, either independently or collaboratively. CHARMIE provides a generic robotic solution such that older people can live longer, more independent, and healthier lives.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the
R&D Units Project Scope: UIDB/00319/2020. The author T.R. received funding through a doctoral
scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência
e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry
of Science, Technology and Higher Education and the European Social Fund through the Programa
Operacional do Capital Humano (POCH). The author F.G. received funding through a doctoral
scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência
e a Tecnologia) [grant number SFRH/BD/145993/2019], with funds from the Portuguese Ministry
of Science, Technology and Higher Education and the European Social Fund through the Programa
Operacional do Capital Humano (POCH)
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection
Mobile service robots are increasingly prevalent in human-centric, real-world
domains, operating autonomously in unconstrained indoor environments. In such a
context, robotic vision plays a central role in enabling service robots to
perceive high-level environmental features from visual observations. Despite
the data-driven approaches based on deep learning push the boundaries of vision
systems, applying these techniques to real-world robotic scenarios presents
unique methodological challenges. Traditional models fail to represent the
challenging perception constraints typical of service robots and must be
adapted for the specific environment where robots finally operate. We propose a
method leveraging photorealistic simulations that balances data quality and
acquisition costs for synthesizing visual datasets from the robot perspective
used to train deep architectures. Then, we show the benefits in qualifying a
general detector for the target domain in which the robot is deployed, showing
also the trade-off between the effort for obtaining new examples from such a
setting and the performance gain. In our extensive experimental campaign, we
focus on the door detection task (namely recognizing the presence and the
traversability of doorways) that, in dynamic settings, is useful to infer the
topology of the map. Our findings are validated in a real-world robot
deployment, comparing prominent deep-learning models and demonstrating the
effectiveness of our approach in practical settings
Averting Robot Eyes
Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms.
We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology
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