论文标题
深度：学习基于激光雷达的地方识别的Roto translation不变代表
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
论文作者
论文摘要
基于激光雷达的位置识别是循环封闭检测和重新定位的流行。近年来，深度学习带来了改进，通过可学习的功能提取来确认认可。但是，当机器人重新访问以前的差异差异时，这些方法会退化。为了应对挑战，我们建议从LiDAR扫描中学习Roto-Translation不变的表示，以便以不同的视角访问同一位置的机器人可以具有相似的表示。深处有两个键：从辛图中提取该特征，并且该特征是通过幅度频谱聚合的。这两个步骤以歧视和旋转转换不变性保持最终表示。此外，我们指出该地点识别是一个单一的学习问题，每个地方都是班级，利用关系学习以建立表示形式相似性。大量实验是在公共数据集上进行的，验证了每个提出的组件的有效性，并表明深度表现优于比较方法，尤其是在数据集级别的概括中。
LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with large perspective difference. To address the challenge, we propose DeepRING to learn the roto-translation invariant representation from LiDAR scan, so that robot visits the same place with different perspective can have similar representations. There are two keys in DeepRING: the feature is extracted from sinogram, and the feature is aggregated by magnitude spectrum. The two steps keeps the final representation with both discrimination and roto-translation invariance. Moreover, we state the place recognition as a one-shot learning problem with each place being a class, leveraging relation learning to build representation similarity. Substantial experiments are carried out on public datasets, validating the effectiveness of each proposed component, and showing that DeepRING outperforms the comparative methods, especially in dataset level generalization.
