• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.11-20
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• 2018

Applications about systems integration of sensors and virtual environments have been developed increasingly. Accordingly, there is a need for the ability to represent, control, and manage physical sensors directly in a 3D virtual environment. In this research, a method of representing physical sensor devices in a 3D virtual environment has been defined using mixed and augmented reality, including virtual and real worlds, where sensors and virtual objects co-exist. The research is intended to control and manage various physical sensors through data sharing and interchange between heterogeneous computing environments. In order to achieve this, general sensor types have been classified, and a sensor based 3D scene graph for representing the functions of sensors has been defined. In addition, a sensor data model has been defined using the scene graph. Finally, a sensor 3D viewer has been implemented based on the scene graph and the data model so as to simulate the functions of sensors in indoor and outdoor 3D environments.

• Advances in robotics research
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• v.1 no.2
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• pp.127-140
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• 2014

This work presents an IR-based system for parking assistance and obstacle detection in the automotive field that employs the Microsoft Kinect camera for fast 3D point cloud reconstruction. In contrast to previous research that attempts to explicitly identify obstacles, the proposed system aims to detect "reachable regions" of the environment, i.e., those regions where the vehicle can drive to from its current position. A user-friendly 2D traversability grid of cells is generated and used as a visual aid for parking assistance. Given a raw 3D point cloud, first each point is mapped into individual cells, then, the elevation information is used within a graph-based algorithm to label a given cell as traversable or non-traversable. Following this rationale, positive and negative obstacles, as well as unknown regions can be implicitly detected. Additionally, no flat-world assumption is required. Experimental results, obtained from the system in typical parking scenarios, are presented showing its effectiveness for scene interpretation and detection of several types of obstacle.