Real time 3D localization and mapping for USAR robotic application

Bedkowski, Janusz; Masłowski, Andrzej; Cubber, Geert De

doi:10.1108/01439911211249751

Cited by 26 publications

(13 citation statements)

References 28 publications

(45 reference statements)

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“…In presented framework, we use ICP from PCL library and ICP improved by parallel nearest neighbourhood search and SVD optimisation inspired by 3DTK framework. More information is available here [5].…”

Section: Iterative Closest Point (Icp)mentioning

confidence: 99%

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Bedkowski

Röhling

Hoeller

et al. 2017

Foundations of Computing and Decision Sciences

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Abstract. This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.

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Section: Iterative Closest Point (Icp)mentioning

confidence: 99%

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Bedkowski

Röhling

Hoeller

et al. 2017

Foundations of Computing and Decision Sciences

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“…This algorithmic component prepares the 3D data for further analysis. For efficient NNS (nearest neighborhood search), a regular grid decomposition (RGD) strategy is used, as explained in our previous work (Bedkowski, Maslowski, & de Cubber, ). The implementation allows us to perform calculations for each 3D point in parallel.…”

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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“…This algorithm performs desirably in reconstructing noisy Kinect scans for large indoor scenes. These approaches often require a powerful graphic hardware to carry the parallel computation or need an assisting hardware to adjust the error generated by the ICP algorithm (Lee et al (2012); Bedkowski et al (2012)). This usually significantly increases the cost of the robot.…”

Section: Dense Matching Approachesmentioning

confidence: 99%

A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM

Zheng

Hong

Zhang

et al. 2016

Computer-Aided Design

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This paper presents a novel multi-frame graph matching algorithm for reliable partial alignments among point clouds. We use this algorithm to stitch frames for 3D environment reconstruction. The idea is to utilize both descriptor similarity and mutual spatial coherency of features existed in multiple frames to match these frames. The proposed multiframe matching algorithm can extract coarse correspondence among multiple point clouds more reliably than pairwise matching algorithms, especially when the data are noisy and the overlap is relatively small. When there are insufficient consistent features appeared in all these frames, our algorithm reduces the number of frames to match to deal with it adaptively. Hence, it is particularly suitable for cost-efficient robotic Simultaneous Localization and Mapping (SLAM). We design a prototype system integrating our matching and reconstruction algorithm on a remotely controlled navigation iRobot, equipped with a Kinect and a Raspberry Pi. Our reconstruction experiments demonstrate the effectiveness of our algorithm and design.

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Real time 3D localization and mapping for USAR robotic application

Cited by 26 publications

References 28 publications

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

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

A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM

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