Cleaning multi-storey buildings need to be considered while developing autonomous service robots. In this paper, we introduce a novel reconfigurable platform called sTetro with the abilitiesto navigate on the floor as well as to detect then climb the staircase autonomously. To this end, an operational framework for this cleaning robot that leverages on customized deep convolution neural network (DCNN) and the RGBD camera to locate staircases in the 3D prebuilt map and then to plan trajectories by maximizing area coverage for both floor and staircase in the multi-storey environments is proposed. While building a 3D map, the staircase location is identified at the 3D point close to the center of the staircase first step using a contour detection algorithm from the boundary of the detected staircase by DCNN. The robot follows the planned trajectory to clear the floor then approaching the staircase location accurately to execute the climbing mode while cleaning the staircase to reach the next floor. The proposed methods archive the high accuracy in identifying the presence of the different staircase types, and the first step locations. Moreover, the multi-storey building evaluations have demonstrated the efficiency of the sTetro in terms of the area coverage both staircase and floor free space.

在开发自动服务机器人时，需要考虑清洁多层建筑。在本文中，我们介绍了一种称为sTetro的新型可重构平台，该平台具有在地板上导航以及自动检测然后爬楼梯的能力。为此，该清洁机器人的操作框架将利用定制的深度卷积神经网络（DCNN）和RGBD摄像机在3D预制地图中定位楼梯，然后通过最大程度地覆盖地板和楼梯的区域覆盖范围来规划轨迹提出了多层环境。在构建3D地图时，使用轮廓检测​​算法通过DCNN从检测到的楼梯的边界，在靠近楼梯第一步的3D点处识别楼梯位置。机器人遵循计划的轨迹清理地板，然后精确地接近楼梯位置以执行爬升模式，同时清洁楼梯以到达下一层。所提出的方法在识别不同楼梯类型和第一步位置的存在方面具有很高的准确性。此外，多层建筑评估证明了sTetro在楼梯和地面自由空间的覆盖面积方面的效率。