论文标题
一个统治所有这些的戒指：ra缩式识别，方向和翻译估计
One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation
论文作者
论文摘要
基于激光雷达的全球本地化是移动机器人的基本问题。它由两个阶段组成：位置识别和姿势估计，可产生当前的方向和翻译，仅使用当前扫描作为查询和地图扫描数据库。受公认位置的定义的启发，我们认为良好的全球定位解决方案应以较低的位置密度保持姿势估计精度。遵循这个想法，我们提出了一个新的框架，用于基于稀疏的地方的全球本地化，该框架利用所有子任务利用统一且无学习的表示radon sinogram（ring）。基于理论推导，提出了一个不变的描述符和方向不变度的度量标准，以实现位置识别，从而实现了可认证的鲁棒性，以针对任意方向以及查询和地图扫描之间的大量翻译。此外，我们还利用环的属性提出了一个全局收敛求解器，以实现方向和翻译估计，并到达全球本地化。对拟议的基于环的框架的评估验证了可行性，即使在较低的位置密度下也证明了卓越的性能。
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density.
