用于视觉位置识别和导航的全向CNN

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摘要翻译：
$$视觉位置识别具有挑战性，尤其是当只有几个位置示例时。为了解决这一难题，我们考虑了基于全方位摄像机的位置识别方法，并提出了一种新的全方位卷积神经网络（O-CNN）来处理摄像机姿态的严重变化。给定一个视觉输入，O-CNN的任务不是检索匹配的位置样本，而是检索最近的位置样本，并估计输入与最近的位置之间的相对距离。利用估计相对距离的能力，提出了一种启发式策略来导航机器人到检索到的最近位置。请注意，该网络的设计是为了利用全方位视图，结合了圆形填充和旋转不变性。为了训练一个强大的O-CNN，我们建立了一个大规模训练的虚拟世界。我们还提出了一种连续提升的结构化特征嵌入损失来有效地学习距离的概念。最后，我们的实验结果证实了我们的方法在虚拟世界和现实世界的数据集上都达到了最先进的精度和速度。
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英文标题：
《Omnidirectional CNN for Visual Place Recognition and Navigation》
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作者：
Tsun-Hsuan Wang, Hung-Jui Huang, Juan-Ting Lin, Chan-Wei Hu, Kuo-Hao
  Zeng, Min Sun
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  $ $Visual place recognition is challenging, especially when only a few place exemplars are given. To mitigate the challenge, we consider place recognition method using omnidirectional cameras and propose a novel Omnidirectional Convolutional Neural Network (O-CNN) to handle severe camera pose variation. Given a visual input, the task of the O-CNN is not to retrieve the matched place exemplar, but to retrieve the closest place exemplar and estimate the relative distance between the input and the closest place. With the ability to estimate relative distance, a heuristic policy is proposed to navigate a robot to the retrieved closest place. Note that the network is designed to take advantage of the omnidirectional view by incorporating circular padding and rotation invariance. To train a powerful O-CNN, we build a virtual world for training on a large scale. We also propose a continuous lifted structured feature embedding loss to learn the concept of distance efficiently. Finally, our experimental results confirm that our method achieves state-of-the-art accuracy and speed with both the virtual world and real-world datasets.
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PDF链接：
https://arxiv.org/pdf/1803.04228

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关键词：CNN Construction Architecture Experimental Mathematical 摄像机 样本 Visual 距离 distance