Balado Jesús, Martínez-Sánchez Joaquín, Arias Pedro, Novo Ana
Applied Geotechnologies Group, Department Natural Resources and Environmental Engineering, School of Mining and Energy Engineering, University of Vigo, Campus Lagoas-Marcosende, CP 36310 Vigo, Spain.
Sensors (Basel). 2019 Aug 8;19(16):3466. doi: 10.3390/s19163466.
In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D geometric information of the environment. The objective of this work is to use point clouds acquired by Mobile Laser Scanning (MLS) to segment the main elements of road environment (road surface, ditches, guardrails, fences, embankments, and borders) through the use of PointNet. Previously, the point cloud was automatically divided into sections in order for semantic segmentation to be scalable to different case studies, regardless of their shape or length. An overall accuracy of 92.5% has been obtained, but with large variations between classes. Elements with a greater number of points have been segmented more effectively than the other elements. In comparison with other point-by-point extraction and ANN-based classification techniques, the same success rates have been obtained for road surfaces and fences, and better results have been obtained for guardrails. Semantic segmentation with PointNet is suitable when segmenting the scene as a whole, however, if certain classes have more interest, there are other alternatives that do not need a high training cost.
在不久的将来，自动驾驶汽车之间的通信将产生一个传感器网络，使我们能够实时了解道路状况。大多数自动驾驶汽车所基于的激光雷达技术能够获取环境的3D几何信息。这项工作的目标是通过使用点云网络（PointNet），利用移动激光扫描（MLS）获取的点云来分割道路环境的主要元素（路面、沟渠、护栏、围栏、路堤和边界）。此前，为了使语义分割能够扩展到不同的案例研究，无论其形状或长度如何，点云都被自动分成若干部分。总体准确率达到了92.5%，但不同类别之间存在很大差异。点数较多的元素比其他元素分割得更有效。与其他逐点提取和基于人工神经网络的分类技术相比，路面和围栏的成功率相同，护栏的结果更好。当对整个场景进行分割时，使用点云网络进行语义分割是合适的，然而，如果某些类别更受关注，还有其他不需要高训练成本的替代方法。
