9,672 research outputs found
Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
In the last decade, many medical companies and research groups have tried to
convert passive capsule endoscopes as an emerging and minimally invasive
diagnostic technology into actively steerable endoscopic capsule robots which
will provide more intuitive disease detection, targeted drug delivery and
biopsy-like operations in the gastrointestinal(GI) tract. In this study, we
introduce a fully unsupervised, real-time odometry and depth learner for
monocular endoscopic capsule robots. We establish the supervision by warping
view sequences and assigning the re-projection minimization to the loss
function, which we adopt in multi-view pose estimation and single-view depth
estimation network. Detailed quantitative and qualitative analyses of the
proposed framework performed on non-rigidly deformable ex-vivo porcine stomach
datasets proves the effectiveness of the method in terms of motion estimation
and depth recovery.Comment: submitted to IROS 201
Implicit Cooperative Positioning in Vehicular Networks
Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.Comment: 15 pages, 10 figures, in review, 201
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
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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