首页> 外文期刊>Sensors >Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations
【24h】

Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations

机译:基于传感器星座的车祸点云和全景图像配准

获取原文
           

摘要

A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10–0.20 m, and vertical accuracy was approximately 0.01–0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed.
机译:通常使用移动地图系统(MMS)来收集城市道路及其周围的环境数据。激光扫描仪和全景相机是彩信的主要传感器。本文提出了一种基于传感器星座图的点云和全景图像配准的新方法。在分析了传感器星座后,利用特征点(即全球定位系统(GPS)天线和全景相机之间的连接线与水平面的交点)将点云分离为块。使用分割特征点提取中央和侧面激光扫描仪的块。然后,将位于块中的点云与原始点云分离。块中的每个点用于通过共线函数以及不同传感器之间的位置和方向关系在相对全景图像中找到准确的对应像素。针对激光扫描仪与全景相机镜头的对应关系,提出了一种搜索策略,以降低计算复杂度,提高效率。选择了四种不同城市道路类型的案例,以验证该方法的有效性和准确性。结果表明,大多数点云(平均为99.7%)已成功地以全景图像配准。几何评估结果表明,在所有情况下,水平精度约为0.10–0.20 m,垂直精度约为0.01–0.02 m。最后,讨论了影响套准精度的主要因素,包括不同传感器之间的时间同步,系统定位和车速。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号