3D Reconstruction Framework for Multiple Remote Robots on Cloud System

Chu, Phuong Minh; Cho, Seoungjae; Fong, Simon; Park, Yong Woon; Cho, Kyungeun

doi:10.3390/sym9040055

Cited by 12 publications

(13 citation statements)

References 46 publications

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“…In current study, Multi-view stereo (MVS) system is used to 3D reconstruct the engine geometry. MVS data is generally very detailed ( [6,17,20,23]; C. [29]). This technique has not been used in the context of manufacturing or remanufacturing.…”

Section: Data Acquisition Through 3d Reconstructionmentioning

confidence: 99%

“…Currently, the remanufacturing process is performed manually. It is crucial to apply emerging technologies and digital manufacturing systems to remanufacturing, to exploit its full potential, whilst reducing lead time and cost [5,6,8,16]. In current work, initial experiments are performed to assess the feasibility of Videogrammetry to acquire pre-disassembly three dimensional (3D) model of the engine.…”

Section: Introductionmentioning

confidence: 99%

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Low cost three-dimensional virtual model construction for remanufacturing industry

et al. 2018

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Remanufactured products can save up to 80% of production and energy costs whilst generating lower CO 2 emissions. The key success factors for remanufacturing are quality, lead-time and cost. Extensive work within the industry and the detailed analysis of the remanufacturing process has shown that component inspection has significant bearing on overall productivity. Remanufacturing lacks automation because activities are predominantly manual. Automation of remanufacturing process will not only decrease the number of non-remanufacturable components, through decreasing cost and increasing consistency in quality, but also attract industries to design for remanufacture. A digital model of the component is required to automate the disassembly process and move towards industry 4.0 and cyber physical systems. There are several expensive techniques to create a digital model, which are not feasible for the remanufacturing industry. The research paper aims to check feasibility of using Visual Structure for Motion (VFM), a relatively low cost method, to develop a 3D digital model, for automation of the automotive engine (in as received condition) disassembly process using industrial robots. These experiments assess the scientific feasibility of using Videogrammetry to acquire pre-disassembly 3D model of the engine. Multiple 2D images were acquired and processed to find matching common features. The location of the camera was calculated through the matching features, producing a three-dimensional digital representation of the captured volume. A sparse point cloud was initially created and was then converted into a dense 3D point cloud. The 3D point cloud was converted into a meshed model. 2D images were stitched together to create a virtual model of the engine with surface texture and colour. Small features were clearly visible in the 3D model.

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Section: Data Acquisition Through 3d Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low cost three-dimensional virtual model construction for remanufacturing industry

et al. 2018

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“…In recent years, many multimedia applications using multi-sensors have been proposed. These applications are widely used in many areas, such as artificial intelligence in cloud environments [1,2], Internet of Things (IoT) [3], virtual reality [4], and unmanned ground vehicles (UGVs) [5][6][7][8][9][10][11][12]. This study is related to multi-sensor data processing for UGV control.…”

Section: Introductionmentioning

confidence: 99%

“…In both cases, sensors are installed to collect information regarding the environment surrounding the vehicle. In particular, for a remote-controlled robot [9][10][11][12], we usually employ multi-channel laser sensors, two-dimensional (2D) cameras, and Global Positioning System-inertial measurement unit (GPS-IMU) sensors. Each laser range sensor returns a point cloud frame-by-frame.…”

Section: Introductionmentioning

confidence: 99%

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

et al. 2018

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Nowadays, unmanned ground vehicles (UGVs) are widely used for many applications. UGVs have sensors including multi-channel laser sensors, two-dimensional (2D) cameras, Global Positioning System receivers, and inertial measurement units (GPS-IMU). Multi-channel laser sensors and 2D cameras are installed to collect information regarding the environment surrounding the vehicle. Moreover, the GPS-IMU system is used to determine the position, acceleration, and velocity of the vehicle. This paper proposes a fast and effective method for modeling nonground scenes using multiple types of sensor data captured through a remote-controlled robot. The multi-channel laser sensor returns a point cloud in each frame. We separated the point clouds into ground and nonground areas before modeling the three-dimensional (3D) scenes. The ground part was used to create a dynamic triangular mesh based on the height map and vehicle position. The modeling of nonground parts in dynamic environments including moving objects is more challenging than modeling of ground parts. In the first step, we applied our object segmentation algorithm to divide nonground points into separate objects. Next, an object tracking algorithm was implemented to detect dynamic objects. Subsequently, nonground objects other than large dynamic ones, such as cars, were separated into two groups: surface objects and non-surface objects. We employed colored particles to model the non-surface objects. To model the surface and large dynamic objects, we used two dynamic projection panels to generate 3D meshes. In addition, we applied two processes to optimize the modeling result. First, we removed any trace of the moving objects, and collected the points on the dynamic objects in previous frames. Next, these points were merged with the nonground points in the current frame. We also applied slide window and near point projection techniques to fill the holes in the meshes. Finally, we applied texture mapping using 2D images captured using three cameras installed in the front of the robot. The results of the experiments prove that our nonground modeling method can be used to model photorealistic and real-time 3D scenes around a remote-controlled robot.

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“…step that is extremely important for robot operation. Especially, ground cloud segmentation is a pre-processing step for many terrain reconstruction applications [8][9][10]. The ground segmentation result is used for recognizing objects, classifications, and feature extraction.…”

mentioning

confidence: 99%

Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems

Chu

Cho

Park

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionInternet of things (IoT) is growing fast in over the world [1][2][3][4][5][6][7]. In an IoT-based system for the autonomous vehicles, light detection and ranging (Lidar) sensors are often used to collect data of surrounding environments. Furthermore, in human-centric autonomous systems, robots also have several attached cameras and an inertial measurement unitglobal positioning system (IMU-GPS) sensor. In each frame, the Lidar sensor returns a point cloud that describes the terrain around the robot. The data from the Lidar sensor are transferred to a computer and split into two groups: ground and nonground. The first group includes ground points of terrain which a robot can traverse. On the other hand, the second group consists of nonground points which the robot cannot traverse such as cars, trees, walls, etc. If the terrain is sloping such that the autonomous robot cannot traverse it, the corresponding points are clustered into the nonground group. The segmentation of three-dimensional (3D) point cloud ground data is a fundamental AbstractGround segmentation is an important step for any autonomous and remote-controlled systems. After separating ground and nonground parts, many works such as object tracking and 3D reconstruction can be performed. In this paper, we propose an efficient method for segmenting the ground data of point clouds acquired from multi-channel Lidar sensors. The goal of this study is to completely separate ground points and nonground points in real time. The proposed method segments ground data efficiently and accurately in various environments such as flat terrain, undulating/ rugged terrain, and mountainous terrain. First, the point cloud in each obtained frame is divided into small groups. We then focus on the vertical and horizontal directions separately, before processing both directions concurrently. Experiments were conducted, and the results showed the effectiveness of the proposed ground segment method. For flat and sloping terrains, the accuracy is over than 90%. Besides, the quality of the proposed method is also over than 80% for bumpy terrains. On the other hand, the speed is 145 frames per second. Therefore, in both simple and complex terrains, we gained good results and real-time performance.

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3D Reconstruction Framework for Multiple Remote Robots on Cloud System

Cited by 12 publications

References 46 publications

Low cost three-dimensional virtual model construction for remanufacturing industry

Low cost three-dimensional virtual model construction for remanufacturing industry

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems

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