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Ultrasonic ranging and infrared depth profiling for 3D image reconstruction and scene analysis.

机译：超声测距和红外深度​​剖析，用于3D图像重建和场景分析。

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摘要

How to efficiently and precisely recognize and localize a target by generating a real-time 3D high definition image in a complex environment is a very challenging task. Traditionally, a target can be detected using ultrasonic ranging technique. However, the detailed information of the complex environment surrounding the target can not be detected by the ultrasonic ranging technique due to the low resolution of the depth profiling generated. In order to improve the quality of the depth profiling and acquired the detailed information, a robust method is required. Thus, in this study, a robust method has been proposed which combines the infrared sensor with the ultrasonic sensors to localize and identify the target in a complex environment by generating a real-time 3D high definition depth profiling and image reconstruction.;Our proposed method is mainly divided into two steps. First, the target is detected by using ultrasonic ranging algorithm. For this purpose, an ultrasonic array microphones system is developed which is called compact and programmable data acquisition node (CAPTAN) board. This system embedded with a Virtex-4 field-programmable gate array and 52 microphones to receive the ultrasonic signals. In this case, when the target is detected by the ultrasound signal sent from an ultrasound transducer, the reflected signal can be captured by the CAPTAN board, thus the direction of the target can be located and the distance of this target can be calculate simultaneously.;Next, detailed depth information of the whole complex environment is generated by the infrared sensors. A method called structured-light is used for generating the depth information. Kinect device is a good commercial product of infrared sensors which is developed by Microsoft with build-in infrared sensors. The infrared sensors are combined with the results from CAPTAN board to generate the detailed depth information. Finally, based on the detailed depth information, a real-time 3D high definition image reconstruction of the whole complex environment can be performed.;To reduce the processing time of 3D high definition image reconstruction, graphical processing unit (GPU) based acceleration is also integrated in the system. This GPU includes powerful compute unified device architecture (CUDA) for flexible and parallel processing. Parallel processing is implemented by writing the fused float-point multiply-add instructions. The parallel processing can significantly reduce rendering and reshaping time within the 3D image reconstruction procedure. According to the benchmarking results, the entire processing time for the real-time 3D high definition image reconstruction is reduced by 80%. The recognition and the localization of the target benefit a lot from the 3D high definition visualization of the complex environment.
机译：如何通过在复杂环境中生成实时3D高清晰度图像来有效，精确地识别和定位目标是一项非常具有挑战性的任务。传统上，可以使用超声波测距技术检测目标。但是，由于生成的深度剖析的分辨率较低，因此无法通过超声测距技术检测到目标周围复杂环境的详细信息。为了提高深度剖析的质量并获取详细信息，需要一种鲁棒的方法。因此，在这项研究中，提出了一种鲁棒的方法，该方法通过生成实时3D高清晰度深度剖析和图像重建，将红外传感器和超声传感器结合在一起，以在复杂环境中定位和识别目标。主要分为两个步骤。首先，使用超声波测距算法检测目标。为此，开发了一种超声波阵列麦克风系统，称为紧凑型可编程数据采集节点（CAPTAN）板。该系统嵌入了Virtex-4现场可编程门阵列和52个麦克风以接收超声信号。在这种情况下，当通过从超声换能器发送的超声信号检测到目标时，可以通过CAPTAN板捕获反射信号，从而可以定位目标的方向并可以同时计算该目标的距离。 ；接下来，红外传感器生成整个复杂环境的详细深度信息。一种称为结构光的方法用于生成深度信息。 Kinect设备是红外传感器的良好商业产品，它是由Microsoft开发的具有内置红外传感器的产品。红外传感器与CAPTAN板的结果相结合，以生成详细的深度信息。最后，基于详细的深度信息，可以对整个复杂环境进行实时3D高清晰度图像重建;为了减少3D高清晰度图像重建的处理时间，基于图形处理单元（GPU）的加速也是集成在系统中。该GPU包括强大的计算统一设备架构（CUDA），可进行灵活，并行的处理。并行处理是通过编写融合的浮点乘加指令来实现的。并行处理可以显着减少3D图像重建过程中的渲染和重塑时间。根据基准测试结果，实时3D高清晰度图像重建的整个处理时间减少了80％。目标的识别和定位受益于复杂环境的3D高清晰度可视化。

著录项

  • 作者

    Jia, Weidi.;

  • 作者单位

    Illinois Institute of Technology.;

  • 授予单位 Illinois Institute of Technology.;
  • 学科 Engineering Electronics and Electrical.;Engineering Computer.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 101 p.
  • 总页数 101
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:41:07

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