论文标题
壁画：基于LIDAR的位置识别的频域扫描上下文，并具有翻译和旋转不变性
FreSCo: Frequency-Domain Scan Context for LiDAR-based Place Recognition with Translation and Rotation Invariance
论文作者
论文摘要
位置识别在机器人和车辆的重新定位和循环封闭检测任务中起着至关重要的作用。本文为基于激光雷达的位置识别寻求明确定义的全球描述符。与本地描述符相比，全球描述符在城市道路场景中表现出色，但通常依赖观点。为此，我们提出了一个简单而坚固的全局描述符，称为壁画，通过利用傅立叶变换和圆形移位技术，将视点差分分解，并实现翻译和旋转不变性。此外，提出了一种快速的两阶段姿势估计方法，以利用从原始数据中提取的紧凑型2D点云来估计位置回收后的相对姿势。实验表明，在来自多个数据集不同场景的序列上，壁画表现出比同期方法表现出色。代码将在https://github.com/soytony/fresco上公开获取。
Place recognition plays a crucial role in re-localization and loop closure detection tasks for robots and vehicles. This paper seeks a well-defined global descriptor for LiDAR-based place recognition. Compared to local descriptors, global descriptors show remarkable performance in urban road scenes but are usually viewpoint-dependent. To this end, we propose a simple yet robust global descriptor dubbed FreSCo that decomposes the viewpoint difference during revisit and achieves both translation and rotation invariance by leveraging Fourier Transform and circular shift technique. Besides, a fast two-stage pose estimation method is proposed to estimate the relative pose after place retrieval by utilizing the compact 2D point clouds extracted from the original data. Experiments show that FreSCo exhibited superior performance than contemporaneous methods on sequences of different scenes from multiple datasets. Code will be publicly available at https://github.com/soytony/FreSCo.
