The robots working indoors must be able to effectively identify their surroundings to complete the autonomous navigation in different scenes.Traditional approaches realize scene recognition by using visual or radar sensors to match the scene.A method of indoor scene recognition based on 2-D range scanning is proposed.This method extracts the features of range scanning information of the lidar, and Extreme Learning Machine Based on Local Receptive Fields (ELM-LRF) is trained by using extracted samples to classify and identify various indoor scenes.In the simulation environment built by Gazebo, the virtual range scanning data is collected, and then the indoor scene recognition methods are studied.The proposed method is verified by experiments based on the range data provided by DR Dataset.The results show that the recognition accuracy of the proposed method is higher than that of traditional methods.The study of scene recognition based on 2-D range scanning also provides theoretical support and experimental data for autonomous robot navigation.