首页> 外文会议>IEEE International Conference on Robotics & Automation >Towards training-free appearance-based localization: Probabilistic models for whole-image descriptors
【24h】

Towards training-free appearance-based localization: Probabilistic models for whole-image descriptors

机译:迈向无训练的基于外观的定位:全图像描述符的概率模型

获取原文

摘要

Whole image descriptors have been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of arbitrary thresholds limit the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph's functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.
机译:整个图像描述符已显示出对感知变化的显着鲁棒性,特别是与局部特征相比。然而,基于全图像的定位系统通常依赖于启发式方法来确定特定环境中的适当匹配阈值。这些特定于环境的调整要求以及对任意阈值缺乏有意义的解释限制了这些系统的总体适用性。在本文中,我们提出了全图像描述符的贝叶斯概率模型,该模型可以无缝集成到为概率视觉输入设计的定位系统中。我们使用CAT-Graph演示了这种方法,CAT-Graph是最初为FAB-MAP风格的概率输入设计的基于外观的视觉本地化系统。我们证明了使用全图像描述符作为可视输入将CAT-Graph的功能扩展到经历了较大感知变化的环境。我们还提出了一种以在线方式估计全图像概率模型的方法,从而无需进行先前的训练阶段。我们表明,这种在线自动训练方法可以与预训练,手动调整的本地描述符方法相媲美。

著录项

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