论文标题
STD：3D位置识别的稳定三角描述符
STD: Stable Triangle Descriptor for 3D place recognition
论文作者
论文摘要
在这项工作中，我们提出了一个新颖的全球描述符，称为3D位置识别的稳定三角形描述符（STD）。对于三角形，其形状由侧面长度或包含角度的唯一决定。此外，三角形的形状对于刚性转换完全不变。基于此属性，我们首先设计了一种算法，以从3D点云中有效提取本地密钥点，并将这些关键点编码为三角形描述符。然后，通过匹配点云之间描述符的侧面长度（以及其他一些信息）来实现位置识别。从描述符匹配对获得的点对应关系可以在几何验证中进一步使用，从而极大地提高了位置识别的准确性。在我们的实验中，我们将我们提出的系统与公共数据集（即Kitti，NCLT和Complex-ublan）和我们自我收集的数据集（具有非重复稳定的扫描固定固体固定型Lidar）上的其他最先进的系统（即M2DP，扫描上下文）进行了广泛的比较。所有定量结果表明，性病具有更强的适应性，并且在其对应物方面的精度有了很大的提高。为了分享我们的发现并为社区做出贡献，我们在GitHub上开放代码：https：//github.com/hku-mars/std。
In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, we first design an algorithm to efficiently extract local key points from the 3D point cloud and encode these key points into triangular descriptors. Then, place recognition is achieved by matching the side lengths (and some other information) of the descriptors between point clouds. The point correspondence obtained from the descriptor matching pair can be further used in geometric verification, which greatly improves the accuracy of place recognition. In our experiments, we extensively compare our proposed system against other state-of-the-art systems (i.e., M2DP, Scan Context) on public datasets (i.e., KITTI, NCLT, and Complex-Urban) and our self-collected dataset (with a non-repetitive scanning solid-state LiDAR). All the quantitative results show that STD has stronger adaptability and a great improvement in precision over its counterparts. To share our findings and make contributions to the community, we open source our code on our GitHub: https://github.com/hku-mars/STD.
