论文标题
烦恼：签名距离函数图中的本地化
Freetures: Localization in Signed Distance Function Maps
论文作者
论文摘要
机器人系统在先前映射的环境中的定位对于减少估计漂移和重复使用以前构建的地图很重要。现有基于几何定位的技术集中在局部表面几何形状的描述上，通常使用尖云作为基础表示。我们提出了一个基于几何定位的系统，该系统直接从隐式表面表示：签名距离函数（SDF）中提取特征。 SDF通过空间连续变化，这使提出的系统可以提取和利用描述表面和自由空间的功能。通过在公共数据集上的评估，我们证明了这种方法的灵活性，并显示了与最先进的手工表面描述符相对于最先进的描述符的本地化性能的提高。我们在RGB-D数据集上的平均提高约为12％，基于激光雷达的数据集的平均提高约为18％。最后，我们演示了我们在先前建造的搜救培训地图中构建的配备激光雷达的MAV的系统。
Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped MAV within a previously built map of a search and rescue training ground.
