3D Point Cloud-Based Virtual Environment for Safe Testing of Robot Control Programs: Measurement Range Expansion through Linking of Multiple Kinect v2 Sensors

Hisahara, Hiroshi; Hane, Seigen; Takemura, Hiroshi; Chin, Yuki; Ogitsu, Takeki; Mizoguchi, Hiroshi

doi:10.1109/isms.2015.19

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Although the 3D processing methods proposed in this paper apply also to Kinect v1 data, the increased quality offered by the new Kinect generation v2 [22] naturally led to its adoption in our platform, which currently supports both versions. A few works regarding capturing with multiple Kinects v2 can be found in the literature [23], [24]. In a work [23] slightly relevant to ours, Kinects are used to capture a static room-sized scene into a virtual 3D model for safe testing of robot control programs.…”

Section: A Previous Relevant Workmentioning

confidence: 99%

“…A few works regarding capturing with multiple Kinects v2 can be found in the literature [23], [24]. In a work [23] slightly relevant to ours, Kinects are used to capture a static room-sized scene into a virtual 3D model for safe testing of robot control programs. In that paper, however, neither the automatic calibration of the sensors nor the real-time capturing and reconstruction of dynamic scenes is addressed.…”

Section: A Previous Relevant Workmentioning

confidence: 99%

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Abstract-The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways. The main elements of an integrated platform, which target tele-immersion and future 3D applications, are described in this paper, addressing the tasks of real-time capturing, robust 3D human shape/appearance reconstruction, and skeletonbased motion tracking. More specifically, initially, the details of a multiple RGB-depth (RGB-D) capturing system are given, along with a novel sensors' calibration method. A robust, fast reconstruction method from multiple RGB-D streams is then proposed, based on an enhanced variation of the volumetric Fourier transform-based method, parallelized on the Graphics Processing Unit, and accompanied with an appropriate texturemapping algorithm. On top of that, given the lack of relevant objective evaluation methods, a novel framework is proposed for the quantitative evaluation of real-time 3D reconstruction systems. Finally, a generic, multiple depth stream-based method for accurate real-time human skeleton tracking is proposed. Detailed experimental results with multi-Kinect2 data sets verify the validity of our arguments and the effectiveness of the proposed system and methodologies.

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Section: A Previous Relevant Workmentioning

confidence: 99%

Section: A Previous Relevant Workmentioning

confidence: 99%

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

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et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Three‐dimensional mapping and immersive human–robot interfacing utilize Kinect‐style depth cameras and virtual reality for agricultural mobile robots

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et al. 2024

Journal of Field Robotics

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Navigation and autonomous operation in farmland or greenhouse environments present significant challenges for agricultural mobile robots on account of unstructured scene conditions and complex task requirements. The immersive interfaces based on human–robot interaction have been extensively developed, which bridge the high‐level decision of human beings and the precise sensing and high motion ability of robots. In this study, we propose a human–robot interaction framework based on three‐dimensional mapping and virtual reality (VR) visualization. On the construction client, dense three‐dimensional point‐cloud maps are created based on the simultaneous localization and mapping real‐time appearance‐based mapping algorithm with a Kinect‐style depth camera. Subsequently, point‐cloud filtering and mesh creation methods are introduced for postprocessing the map models. A data communication network is employed to transmit the optimized models to the VR‐based exploration client. Utilizing VR visualization, the operator can gain an intuitive and comprehensive understanding of the environments to be explored. The three‐dimensional agricultural scenes are mapped into VR models through the system, which can combine the physical worlds with the VR spaces in a better manner. Compared with the video streaming‐based method for three‐dimensional mapping, the map models with colors and textures are entirely displayed in the VR headset, thus providing robust and global human–robot interaction interfaces and enhancing the immersive exploration for human beings to a considerable extent. Experimental results indicate that the three‐dimensional map models exhibit relatively sufficient construction integrity and yield favorable optimization effects. Additionally, according to a user questionnaire survey, immersive VR‐based interfaces can be more effectively utilized for the three‐dimensional mapping of mobile robots. Research evidence and results demonstrate that our proposed system framework offers positive assistance and support for enhancing human–robot interaction and executing specific robotic tasks in unstructured field agricultural environments. Code implementation and related data sets are shared at the link https://github.com/WangDongBUAA/Mapping_Interfacing.

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Action recognition robust to position changes using skeleton information and SVM

Zhou

Fan

2016

2016 International Conference on Robotics and Automation Engineering (ICRAE)

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3D Point Cloud-Based Virtual Environment for Safe Testing of Robot Control Programs: Measurement Range Expansion through Linking of Multiple Kinect v2 Sensors

Cited by 3 publications

References 6 publications

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

Three‐dimensional mapping and immersive human–robot interfacing utilize Kinect‐style depth cameras and virtual reality for agricultural mobile robots

Action recognition robust to position changes using skeleton information and SVM

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