Mapping the environment for robot navigation is an important and challenging task in SLAM (Simultaneous Localization And Mapping). Lidar sensor can produce near accurate 3D map of the environment in real time in form of point clouds. Though the point cloud data is adequate for building the map of the environment, processing millions of points in a point cloud is found to be computationally expensive. In this paper, we propose a fast algorithm that can be used to extract semantically labelled surface segments from the cloud in real time for direct navigational use or for higher level contextual scene reconstruction. First, a single scan from a spinning Lidar is used to generate a mesh of sampled cloud points. The generated mesh is further used for surface normal computation of a set of points on the basis of which surface segments are estimated. A novel descriptor is proposed to represent the surface segments. This descriptor is used to determine the surface class (semantic label) of the segments with the help of a classifier. These semantic surface segments can be further utilized for geometric reconstruction of objects in the scene or for optimized trajectory planning of a robot. The proposed method is compared with a number of point cloud segmentation methods and state of the art semantic segmentation methods to demonstrate its efficacy in terms of speed and accuracy.

映射机器人导航环境是SLAM（同时定位和映射）中一项重要且具有挑战性的任务。激光雷达传感器可以点云的形式实时生成接近准确的3D环境地图。尽管点云数据足以用于构建环境图，但是发现在点云中处理数百万个点在计算上非常昂贵。在本文中，我们提出了一种快速算法，可用于从云中实时提取带有语义标记的表面片段，以直接用于导航或用于更高级别的上下文场景重构。首先，来自旋转的激光雷达的单次扫描用于生成采样的浊点网格。所生成的网格还用于基于估计的曲面段的一组点的曲面法线计算。提出了一种新颖的描述符来表示表面片段。该描述符用于在分类器的帮助下确定线段的表面类别（语义标签）。这些语义表面片段可以进一步用于场景中对象的几何重构或用于机器人的优化轨迹规划。将该方法与许多点云分割方法和最新的语义分割方法进行了比较，以证明其在速度和准确性方面的有效性。这些语义表面片段可以进一步用于场景中对象的几何重构或用于机器人的优化轨迹规划。将该方法与许多点云分割方法和最新的语义分割方法进行了比较，以证明其在速度和准确性方面的有效性。这些语义表面片段可以进一步用于场景中对象的几何重构或用于机器人的优化轨迹规划。将该方法与许多点云分割方法和最新的语义分割方法进行了比较，以证明其在速度和准确性方面的有效性。