• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of robotic systems >Generation and Visualization of Large-Scale Three-Dimensional Reconstructions from Underwater Robotic Surveys
【24h】

Generation and Visualization of Large-Scale Three-Dimensional Reconstructions from Underwater Robotic Surveys

机译：水下机器人勘测进行大规模三维重构的生成和可视化

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Robust, scalable simultaneous localization and mapping (SLAM) algorithms support the Successful deployment of robots in real-world applications. In many cases these platforms deliver vast amounts of sensor data from large-scale, unstructured environments. These data may be difficult to interpret by end users without further processing and suitable visualization tools. We present a robust, automated system for large-scale three-dimensional (3D) reconstruction and visualization that takes stereo imagery from an autonomous underwater vehicle (AUV) and SLAM-based vehicle poses to deliver detailed 3D models of the seafloor in the form of textured polygonal meshes. Our system must cope with thousands of images, lighting conditions that create visual seams when texturing, and possible inconsistencies between stereo meshes arising from errors in calibration, triangulation, and navigation. Our approach breaks down the problem into manageable stages by first estimating local structure and then combining these estimates to recover a composite georeferenced structure using SLAM-based vehicle pose estimates. A texture-mapped surface at multiple scales is then generated that is interactively presented to the user through a visualization engine. We adapt established solutions when possible, with an emphasis on quickly delivering approximate yet visually consistent reconstructions on standard computing hardware. This allows scientists on a research cruise to use our system to design follow-up deployments of the AUV and complementary instruments. To date, this system has been tested on several research cruises in Australian waters and has been used to reliably generate and visualize reconstructions for more than 60 dives covering diverse habitats and representing hundreds of linear kilometers of survey.
机译：健壮，可扩展的同时定位和映射（SLAM）算法支持在实际应用中成功部署机器人。在许多情况下，这些平台可从大规模，非结构化环境中提供大量传感器数据。如果没有进一步的处理和合适的可视化工具，最终用户可能很难解释这些数据。我们提供了一个强大的自动化系统，用于大规模三维（3D）重建和可视化，该系统从自动水下航行器（AUV）和基于SLAM的车辆姿态中获取立体图像，以以下形式提供详细的海底3D模型：纹理的多边形网格。我们的系统必须处理成千上万的图像，纹理化时会形成视觉接缝的照明条件，以及由于校准，三角剖分和导航错误而导致的立体网格之间可能存在的不一致。我们的方法通过首先估计局部结构，然后将这些估计组合起来以使用基于SLAM的车辆姿态估计来恢复复合地理参考结构，将问题分解为可管理的阶段。然后生成多个比例的纹理映射表面，该表面通过可视化引擎交互呈现给用户。我们会尽可能调整既定的解决方案，重点是在标准计算硬件上快速交付近似但视觉上一致的重建。这使研究航行中的科学家可以使用我们的系统来设计AUV和辅助仪器的后续部署。迄今为止，该系统已经在澳大利亚水域的几次研究航行中进行了测试，并已用于可靠地生成和可视化重建图，涵盖了覆盖不同栖息地，代表数百线性公里的60多次潜水。

著录项

  • 来源
    《Journal of robotic systems》 |2010年第1期|21-51|共31页
  • 作者

    Matthew Johnson-Roberson; Oscar Pizarro; Stefan B. Williams; Ian Mahon;

  • 作者单位

    Australian Centre for Field Robotics, University of Sydney, Sydney 2006, New South Wales, Australia;

    Australian Centre for Field Robotics, University of Sydney, Sydney 2006, New South Wales, Australia;

    Australian Centre for Field Robotics, University of Sydney, Sydney 2006, New South Wales, Australia;

    Australian Centre for Field Robotics, University of Sydney, Sydney 2006, New South Wales, Australia;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号