基于改进PFPN的语义地图构建方法研究

Building Method of Semantic Map Based on Improved PFPN

针对园区等环境结构性强而全球导航卫星系统(GNSS)信号不稳定的应用场景及无人车应用激光雷达同时定位与建图(SLAM)技术缺乏对于场景的语义理解能力而造成的定位误差问题,提出了一种单目相机与激光雷达融合构建三维语义地图的方法。该方法以园区环境结构性强、车辆行人动态变化高的特征为依据,通过改进的全景特征金字塔网络(PFPN)进行场景视觉语义分割,然后采用像素级融合方法为激光点云提供语义信息,从而有效地去除了正态分布变换(NDT)建图过程中动态目标的干扰,进而提高了无人车SLAM技术在动态环境中的鲁棒性和精确性。在旋风智能无人驾驶平台上进行实验验证,并与原始NDT方法进行了比较,实验结果表明:所提方法能够全面提升建图精度,其中位姿精度最为显著,提高了34.34%;除此之外,建图的点云数量也降低了39.78%,大大提高了建图速度。

Aiming at the parks and other similar environments application scenarios where the scene feature is unique and the global navigation satellite system(GNSS) signal is unstable, as well as the problem that the lack of semantic understanding in Lidar simultaneous localization and mapping(SLAM) results in a localization error of unmanned vehicle, a building method of three-dimensional semantic map with the data fusion of monocular camera and Lidar is proposed. This method is based on the characteristics of strong structural park environment and high dynamic variation of pedestrian in vehicles. The improved panoramic feature pyramid network(PFPN) is used for scene visual semantic segmentation, and then the pixel level fusion method is used to provide semantic information for the laser point cloud,so as to effectively remove the interference of dynamic targets in the process of normal distribution transformation(NDT) mapping, and then improve the robustness and accuracy of unmanned vehicle SLAM technology in the dynamic environment. Experimental validation is carried out on the cyclone intelligent self-driving platform and compared with the original NDT method, the experimental results show that the proposed method is able to improve the construction accuracy comprehensively, with the most significant improvement of 34. 34% in positional accuracy;besides that, the number of point clouds for building the diagram is also reduced by 39. 78%, which greatly improves the construction speed.