12,725 research outputs found
Towards Odor-Sensitive Mobile Robots
J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012
Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical
ones when operating in real environments. Until now, these sensorial systems mostly relied on range
sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely
been employed, they can provide a complementary sensory information, vital for some applications, as
with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities
and also reviews some of the hurdles that are preventing smell from achieving the importance of other
sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status
on the three main fields within robotics olfaction: the classification of volatile substances, the spatial
estimation of the gas dispersion from sparse measurements, and the localization of the gas source within
a known environment
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Localization from semantic observations via the matrix permanent
Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Simultaneous localization and map-building using active vision
An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio
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
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