A novel autonomous staircase cleaning system with robust 3D-Deep Learning-based perception technique for Area-Coverage

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Cleaning the staircases is the next big leap for every commercial cleaning robot in order to accomplish a full-fledged cleaning of a constructed buildings. Such an effort could be witnessed in the academic literature where a robotic system can autonomously clean the staircase by ascending. However, none of the existing staircase traversing platforms demonstrated the ability to perform both ascending and descending motion while cleaning, which can significantly improvise the overall robot’s performance. In this paper, we propose a novel reconfigurable cleaning robotic platform called sTetro_plotter, which can perform both ascend and descend motion in the staircases. Pointedly, in this work, we presented a perception framework for the developed robot to traverse on the staircase and perform area coverage autonomously. The framework was constructed with a pointNet++ based feature extractor and classification and regression network to generate a bounding box on the targeted feature. Also, we discussed the process of a staircase descending through tracking the generated bounding box. We implemented a sweeping-based lidar device that can generate a 3D point cloud by sensing its environment. We evaluated the performance of the proposed robot and its perception system through conducting experiments in real-world scenarios. The experimental trials successfully demonstrate the ability of the sTetro_plotter robot to perform autonomous area coverage while traversing on the staircase using the developed perception framework.