Mobile terrestrial lidar data to detect traffic sign and light pole/ Dados de laser terrestre móvel para detectar placas de sinalização e postes de iluminação

This paper introduces a method for detecting and classifying vertical objects from a mobile terrestrial laser scanner point cloud. The paper concentrates on the classification of the top of the poles, where shields or lamps are installed. First, the variance-covariance matrix of each segmented object is computed. Then the eigenvalues and eigenvector of this matrix are derived. The 3D coordinates of each point are then transformed using the principal components transform in order to compute new features in this new space. In the second step the distribution of the three eigenvalues of the different classes in the eigenvalues space is analysed. Is it deduced that similar objects align in this space, allowing proposing a classification rule based on the distance to the lines. An experiment was performed to verify the approach performance. In the classification of different objects, the global accuracy reached 75%. When the classification was more general, separating just flat from three-dimensional objects the accuracy reached 94%. From the obtained results it can be concluded that the proposed method is feasible and allows separating objects according to its shap

功能性规则约束下的三维点云道路设施语义标注

三维场景的语义标注研究是机器视觉、摄影测量以及机器学习等领域的热门研究课题.但基于移动激光扫描数据的道路设施精确解释仍处于瓶颈期.提出一种基于逻辑关系和功能性对道路设施进行语义标注的新方法,先总结制定道路设施相关的特征符号和规则,再根据所定义的规则功能对点云数据进行语义标注.基于该方法对国内某中等城市道路点云数据进行了相当详尽的解释,正确提取了93%的杆状物体,并全部正确识别.对于杆状物体的附件(如灯头、交通标志等),基本正确识别且有效标记.与改进的RANSAC算法相比,该方法提供了一个较好的解决方案,有助于在城市环境中自动绘制详细的道路设施.国家自然科学基金(41771439);;国家重点研发计划项目(2016YFB0502300);;江苏省研究生科研与实践创新计划项目(KYCX18_1206

Mapping of Road Facilities and Information on High Definition Maps using Mobile Mapping System

学位の種別: 修士University of Tokyo(東京大学