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Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud
Cho, Seoungjae,Kim, Jonghyun,Ikram, Warda,Cho, Kyungeun,Jeong, Young-Sik,Um, Kyhyun,Sim, Sungdae Hindawi Publishing Corporation 2014 The Scientific World Journal Vol.2014 No.-
<P>A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.</P>
Simulation framework of ubiquitous network environments for designing diverse network robots
Cho, Seoungjae,Fong, Simon,Park, Yong Woon,Cho, Kyungeun North-Holland 2017 Future generations computer systems Vol.76 No.-
<P><B>Abstract</B></P> <P>Smart homes provide residents with services that offer convenience using sensor networks and a variety of ubiquitous instruments. Network robots based on such networks can perform direct services for these residents. Information from various ubiquitous instruments and sensors located in smart homes is shared with network robots. These robots effectively help residents in their daily routine by accessing this information. However, the development of network robots in an actual environment requires significant time, space, labor, and money. A network robot that has not been fully developed may cause physical damage in unexpected situations. In this paper, we propose a framework that allows the design and simulation of network robot avatars and a variety of smart homes in a virtual environment to address the above problems. This framework activates a network robot avatar based on information obtained from various sensors mounted in the smart home; these sensors identify the daily routine of the human avatar residing in the smart home. Algorithms that include reinforcement learning and action planning are integrated to enable the network robot avatar to serve the human avatar. Further, this paper develops a network robot simulator to verify whether the network robot functions effectively using the framework.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We proposed a framework to simulate a network robot in a virtual smart home. </LI> <LI> A network robot agent identifies daily routines of a resident and executes service. </LI> <LI> The framework shows a network robot could help and reduce tasks of a human agent. </LI> <LI> The simulator verified the framework reduces costs of developing network robots. </LI> </UL> </P>
A REAL-LIFE SIMULATION MODEL FOR NPC PERSONALITY WITH GAUSSIAN DISTRIBUTION
Kyungeun Cho,Song Wei,Kyhyun Um 한국멀티미디어학회 2007 한국멀티미디어학회 국제학술대회 Vol.2007 No.-
This paper describes a behavior planning framework in simulation game based on character personality using proposed Gaussian random distribution. Along with the data flowing process in our planning framework, NPC can generate behavior planning autonomously according to the dynamic environment information resulted by human player. Further more, we illuminate applying Gaussian probabilistic function for real-life action simulation in time domain with the expected value estimated by behavior planning during thinking process. To elucidate the mechanism of the framework, we simulated it in a restaurant simulation game.
AN EXTRACTION SCHEME OF DISEASED REGIONS FROM BRAIN MR IMAGES
Kyungeun Cho,Kyhyun Um 한국멀티미디어학회 2006 한국멀티미디어학회 국제학술대회 Vol.2006 No.-
This paper proposes a method of extracting diseased regions from brain MR images. The first stage of the method segments the areas of the brain structure by differentiating the negative image from the original image. After segmenting the white matter from the brain MR image, the remaining area is segmented. Cerebrospinal fluid regions are extracted and diseased areas are segmented according to the thickness of the white-grey matter. Finally, the features of the diseased areas in the medical images are extracted for 3D spatial indexing. This paper has confirmed the effectiveness of this method by showing that various diseased areas can be correctly extracted through experiments with sets of brain MR images
Optimization of Multiple Sensor Data Pipeline for Real-time 3D Terrain Reconstruction
Seoungjae Cho,Seongjo Lee,Kyhyun Um,Kyungeun Cho,Sungdae Sim,Yong Woon Park 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
Remote-control technology is required in an unmanned vehicle such that it can replace humans for executing tasks in various extreme environments. In particular, a remote scene must be reconstructed using 3D meshes for enabling a user to remotely control an unmanned vehicle easily and intuitively. To this end, a large amount of multiple sensor data would be processed using various algorithms in real time. Considering the limited hardware specifications in extreme environments, it is difficult to implement 3D terrain reconstruction in high-quality and real time. This paper proposes the optimization of the architecture of a multiple-sensor-data pipeline. The improved performance resulting from the optimized architecture was analyzed through experimental comparison with a non-optimized system.