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首页> 外文期刊>Computer vision and image understanding >Localization and tracking in known large environments using portable real-time 3D sensors
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Localization and tracking in known large environments using portable real-time 3D sensors

机译：使用便携式实时3D传感器在已知的大型环境中进行定位和跟踪

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摘要

Ego-motion estimation and localization in large environments are key components in any assistive technology for real-time user orientation and navigation. We consider the case where a large known environment is explored without a priori assumptions on the initial location. In particular we propose a framework that uses a single portable 3D sensor to solve the place recognition problem and continuously tracks its position even when leaving the known area or when significant changes occur in the observed environment We cast the place recognition step as a classification problem and propose an efficient search space reduction considering only navigable areas where the user can be localized. Classification hypotheses are then discarded exploiting temporal consistency w.r.t. a relative tracker that exploits only the sensor input data. The solution uses a compact classifier whose representation scales well with the map size. After being localized, the user is continuously tracked exploiting the known environment using an efficient data structure that provides constant access time for nearest neighbor searches and that can be streamed to keep only the local region close to the last known position in memory. Robust results are achieved by performing a geometrically stable selection of points, efficiently filtering outliers and integrating the relative tracker based on previous observations. We experimentally show that such a framework provides good localization results and that it scales well with the environment map size yielding real-time performance for both place recognition and tracking.
机译：大型环境中的自我运动估计和本地化是用于实时用户定向和导航的任何辅助技术的关键组成部分。我们考虑在没有先验假设的情况下探索大型已知环境的情况。特别是，我们提出了一个框架，该框架使用单个便携式3D传感器来解决位置识别问题，即使离开已知区域或在观察到的环境中发生重大变化时也可以连续跟踪其位置。我们将位置识别步骤归类为分类问题，提出仅考虑可定位用户的可导航区域的有效搜索空间缩减。然后利用时间一致性w.r.t.丢弃分类假设。一个仅利用传感器输入数据的相对跟踪器。该解决方案使用了一个紧凑的分类器，该分类器的表示与地图大小很好地缩放。定位后，将使用有效数据结构连续跟踪用户利用已知环境，该数据结构为最近的邻居搜索提供恒定的访问时间，并且可以进行流传输以仅使本地区域保持在内存中的最后一个已知位置附近。通过执行几何上稳定的点选择，有效过滤异常值并基于先前的观察结果对相对跟踪器进行集成，可以获得可靠的结果。我们通过实验证明，这种框架可提供良好的本地化结果，并且可以随着环境地图大小的扩展而很好地缩放，从而为位置识别和跟踪提供实时性能。

著录项

  • 来源
    《Computer vision and image understanding》 |2016年第8期|197-208|共12页
  • 作者

    Carlos Sanchez; Pierluigi Taddei; Simone Ceriani; Erik Wolfart; Vitor Sequeira;

  • 作者单位

    European Commission, Joint Research Centre (JRC), Institute for Transuranium Elements (ITU), Via Enrico Fermi 2749, Ispra (VA), Italy;

    European Commission, Joint Research Centre (JRC), Institute for Transuranium Elements (ITU), Via Enrico Fermi 2749, Ispra (VA), Italy;

    European Commission, Joint Research Centre (JRC), Institute for Transuranium Elements (ITU), Via Enrico Fermi 2749, Ispra (VA), Italy;

    European Commission, Joint Research Centre (JRC), Institute for Transuranium Elements (ITU), Via Enrico Fermi 2749, Ispra (VA), Italy;

    European Commission, Joint Research Centre (JRC), Institute for Transuranium Elements (ITU), Via Enrico Fermi 2749, Ispra (VA), Italy;

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

    Place recognition; Ego-motion; 3D sensors;

    机译：位置识别;自我运动3D传感器;

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