基于二维概率网格的语义地图构建

Semantic Map Construction Based on 2D Probabilistic Grids

在同步定位与地图构建(SLAM)算法构建语义地图的过程中,为解决使用深度学习技术提取的语义信息中含有噪声的问题,提出通过网格的概率表征语义观测的语义匹配法,结合网格匹配和概率更新降低噪声的影响,无需特征点匹配等辅助手段即可增量地构建清晰的地图。基于实车数据构建的地图所包含的斑马线和车道线均清晰准确,证明了该方法的有效性。

In the semantics mapping construction of Simultaneous Localization And Mapping(SLAM)algorithm,to solve the problem that the semantics information extracted by using deep learning technology contains noise,this paper proposes a representation method for semantic observation by probabilistic grid.The influence of noise is reduced by grid matching and probability updating,in this way,a clear map can be incrementally constructed without auxiliary means like features matching.The effectiveness of the proposed method is verified by experiments in which clear zebra crossings and lane lines are contained in the constructed map.