Laser Navigation and Mapping Based on Building Environment Classification

In this paper, we propose a building environment classification method based on the Adaboost algorithm for autonomous environment perception and mapping of mobile robots in unknown building environments. In the proposed method, laser sensor is used to obtain the raster map of local environment, whose features are extracted. Then, the Adaboost algorithm is used to construct a scene classifier by selecting representative boundary points of different scenarios. We use a boundary-based path planning strategy, in which boundary points determine the navigation path of a mobile robot. Experimental results show that a mobile robot can conduct autonomous inspection in unknown building environments. Simultaneously, the detected local raster maps are spliced into a complete building environment map using built-in simultaneous localization and mapping (SLAM) technology to realize autonomous navigation.