面向无人平台建筑内导航的地图轻量化处理与楼梯区域分类方法

Constructing an environment map is a crucial prerequisite for navigation,and a comprehensive and detailed map can effectively assist in planning the optimal motion paths for unmanned ground platforms. To address the issues of data redundancy and difficulty in distinguishing the terrain structures in traditional map construction methods,a lightweight map processing and staircase area classification method for indoor navigation of unmanned ground platforms is proposed. The method first extracts the traversable area for unmanned platforms to reduce data redundancy and removes the outliers based on the distribution characteristics of stair surfaces. Subsequently,a map construction algorithm incorporating edge smoothing is used to generate multi-layer grid maps with clear boundaries,regular shapes,and distinct levels. Then, the stair environment features are extracted, and a Naive Bayes classification algorithm with Laplace smoothing is employed to distinguish and label the structures such as steps and turning platforms. The experimental results show that the maps generated by this method maintain high resolution while reducing the data volume by an order of magnitude compared to traditional point cloud maps,and the macro-precision rate of map classification reaches 91. 3%. Compared with conventional methods,the proposed method can construct more lightweight multi-layer grid maps with terrain classification labels,providing safe and efficient navigation support for unmanned ground platforms.