| Citation: | ZHANG Fukai, RUI Ting, HE Lei, et al. A low-cost indoor passable area modeling method for robots[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2470-2478. doi: 10.13700/j.bh.1001-5965.2019.0393(in Chinese)
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Monocular vision-based simultaneous localization and mapping (SLAM) is a popular technology in the field of robotics in recent years. However, due to the huge computation resource required by reconstruction, mainstream methods are not able to generate meaningful reconstruction of scene in real time on platforms with low computing power. This paper proposes a new fast passable area modeling method for the specific situation of indoor environment and small robots. The method is based on the monocular feature-based SLAM. Firstly, it obtains the road segmentation image through segmentation in the HSV color space with adaptive threshold. Then, the system cross-matches the segmentation with the sparse point cloud generated by SLAM, to obtain the ground plane and accurate ground segmentation area. Finally, it projects the ground segmentation area to the ground plane for dense modeling of the floor. In the experiment of indoor scene, the average calculation speed of the proposed method can reach 21 frames per second, and the speed is about 70% of ORB-SLAM, which can meet the real-time requirements of mobile platforms. The position error for the floor plane is 5.8% on average, and the modeling error of the road width is between 3.5% and 12.8%.
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