A novel 3D sensory system for robot-assisted mapping of cluttered urban search and rescue environments

Zhang, Zhe; Nejat, Goldie; Guo, Hong; Huang, Peisen S.

doi:10.1007/s11370-010-0082-3

Cited by 23 publications

(6 citation statements)

References 37 publications

(47 reference statements)

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“…Mapping of unstructured environments in two and three dimensions is currently a popular research topic. Research is being carried on finding sensor systems for the task (Mobedi & Nejat, ) and mapping strategies and algorithms (Zhang, Nejat, Guo, & Huang, ). Even different means of unusual sensor transportation, such as using canines, are being tested (Tran, Ufkes, Ferworn, & Fiala, ).…”

Section: A Data Management Architecture For Search‐and‐rescue Robotsmentioning

confidence: 99%

Integrated Data Management for a Fleet of Search‐and‐rescue Robots

Balta

Bedkowski

Govindaraj

et al. 2016

Journal of Field Robotics

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Search‐and‐rescue operations have recently been confronted with the introduction of robotic tools that assist the human search‐and‐rescue workers in their dangerous but life‐saving job of searching for human survivors after major catastrophes. However, the world of search and rescue is highly reliant on strict procedures for the transfer of messages, alarms, data, and command and control over the deployed assets. The introduction of robotic tools into this world causes an important structural change in this procedural toolchain. Moreover, the introduction of search‐and‐rescue robots acting as data gatherers could potentially lead to an information overload toward the human search‐and‐rescue workers, if the data acquired by these robotic tools are not managed in an intelligent way. With that in mind, we present in this paper an integrated data combination and data management architecture that is able to accommodate real‐time data gathered by a fleet of robotic vehicles on a crisis site, and we present and publish these data in a way that is easy to understand by end‐users. In the scope of this paper, a fleet of unmanned ground and aerial search‐and‐rescue vehicles is considered, developed within the scope of the European ICARUS project. As a first step toward the integrated data‐management methodology, the different robotic systems require an interoperable framework in order to pass data from one to another and toward the unified command and control station. As a second step, a data fusion methodology will be presented, combining the data acquired by the different heterogenic robotic systems. The computation needed for this process is done in a novel mobile data center and then (as a third step) published in a software as a service (SaaS) model. The SaaS model helps in providing access to robotic data over ubiquitous Ethernet connections. As a final step, we show how the presented data‐management architecture allows for reusing recorded exercises with real robots and rescue teams for training purposes and teaching search‐and‐rescue personnel how to handle the different robotic tools. The system was validated in two experiments. First, in the controlled environment of a military testing base, a fleet of unmanned ground and aerial vehicles was deployed in an earthquake‐response scenario. The data gathered by the different interoperable robotic systems were combined by a novel mobile data center and presented to the end‐user public. Second, an unmanned aerial system was deployed on an actual mission with an international relief team to help with the relief operations after major flooding in Bosnia in the spring of 2014. Due to the nature of the event (floods), no ground vehicles were deployed here, but all data acquired by the aerial system (mainly three‐dimensional maps) were stored in the ICARUS data center, where they were securely published for authorized personnel all over the world. This mission (which is, to our knowledge, the first recorded deployment of an unmanned aerial system by an official governmental i...

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Section: A Data Management Architecture For Search‐and‐rescue Robotsmentioning

confidence: 99%

Integrated Data Management for a Fleet of Search‐and‐rescue Robots

Balta

Bedkowski

Govindaraj

et al. 2016

Journal of Field Robotics

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“…A general overview on robotics in disaster scenarios can be found, for example, in [11]. Again other work looks at sensor systems [12] that are appropriate for generating feasible maps. As shown in [13], for first responders it is important to get a reliable overview of the disaster site and the damages and hazards in order to react correctly.…”

Section: Introductionmentioning

confidence: 99%

A System for Continuous Underground Site Mapping and Exploration

Ferrein¹,

Scholl²,

Neumann³

et al. 2020

Unmanned Robotic Systems and Applications

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3D mapping becomes ever more important not only in industrial mobile robotic applications for AGV and production vehicles but also for search and rescue scenarios. In this chapter we report on our work of mapping and exploring underground mines. Our contribution is two-fold: First, we present our custom-built 3D laser range platform SWAP and compare it against an architectural laser scanner. The advantages are that the mapping vehicle can scan in a continuous mode and does not have to do stop-and-go scanning. The second contribution is the mapping tool mapit which supports and automates the registration of large sets of point clouds. The idea behind mapit is to keep the raw point cloud data as a basis for any map generation and only store all operations executed on the point clouds. This way the initial data do not get lost, and improvements on low-level date (e.g. improved transforms through loop closure) will automatically improve the final maps. Finally, we also present methods for visualization and interactive exploration of such maps.

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“…Our previous research has focused on developing rescue robots that use learning for exploration, navigation and victim identification tasks in USAR environments [1, [10][11][12], and identifying landmarks in USAR scenes for 3D mapping [13][14][15]. In this paper, we present the first feasibility study that investigates the use of deep learning to address the victim identification problem in robotic USAR.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Find Victims in Unknown Cluttered Urban Search and Rescue Environments

et al. 2020

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Purpose of Review We investigate the first use of deep networks for victim identification in Urban Search and Rescue (USAR). Moreover, we provide the first experimental comparison of single-stage and two-stage networks for body part detection, for cases of partial occlusions and varying illumination, on a RGB-D dataset obtained by a mobile robot navigating cluttered USAR-like environments. Recent Findings We considered the single-stage detectors Single Shot Multi-box Detector, You Only Look Once, and RetinaNet and the two-stage Feature Pyramid Network detector. Experimental results show that RetinaNet has the highest mean average precision (77.66%) and recall (86.98%) for detecting victims with body part occlusions in different lighting conditions. Summary End-to-end deep networks can be used for finding victims in USAR by autonomously extracting RGB-D image features from sensory data. We show that RetinaNet using RGB-D is robust to body part occlusions and low-lighting conditions and outperforms other detectors regardless of the image input type.

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A novel 3D sensory system for robot-assisted mapping of cluttered urban search and rescue environments

Cited by 23 publications

References 37 publications

Integrated Data Management for a Fleet of Search‐and‐rescue Robots

Integrated Data Management for a Fleet of Search‐and‐rescue Robots

A System for Continuous Underground Site Mapping and Exploration

Using Deep Learning to Find Victims in Unknown Cluttered Urban Search and Rescue Environments

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