Reliable and accurate localization and mapping are key components of most
autonomous systems. Besides geometric information about the mapped environment,
the semantics plays an important role to enable intelligent navigation
behaviors. In most realistic environments, this task is particularly
complicated due to dynamics caused by moving objects, which can corrupt the
mapping step or derail localization. In this paper, we propose an extension of
a recently published surfel-based mapping approach exploiting three-dimensional
laser range scans by integrating semantic information to facilitate the mapping
process. The semantic information is efficiently extracted by a fully
convolutional neural network and rendered on a spherical projection of the
laser range data. This computed semantic segmentation results in point-wise
labels for the whole scan, allowing us to build a semantically-enriched map
with labeled surfels. This semantic map enables us to reliably filter moving
objects, but also improve the projective scan matching via semantic
constraints. Our experimental evaluation on challenging highways sequences from
KITTI dataset with very few static structures and a large amount of moving cars
shows the advantage of our semantic SLAM approach in comparison to a purely
geometric, state-of-the-art approach.

本文提出了一种基于三维激光雷达测距扫描和神经网络对语义信息进行提取的建图及定位方法，实现了对运动物体的有效滤波和语义信息约束下的扫描匹配，与现有基于几何约束的方法相比，实验结果表明其性能更优。