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摘要
Acquiring semantic information in the surrounding environment is an important task of semantic simultaneous localization and mapping(SLAM). However, the time performance of the system is affected by semantic segmentation or instance segmentation, and the accuracy of the system is reduced while adopting object detection methods. Therefore, a pixel-level segmentation algorithm combining depth map clustering and object detection is proposed in this paper. The positioning accuracy of the current semantic SLAM system is improved with the real-time performance of the system guaranteed. Firstly, the mean filtering algorithm is utilized to repair the invalid points of the depth map and thus the depth information is more reliable. Secondly, object detection is performed on RGB images and K-means clustering is employed for corresponding depth maps, and then the pixel-level object segmentation result is obtained by combining the two results. Finally, the dynamic points in the surrounding environment are eliminated by the results described above, and a complete semantic map without dynamic objects is established. Experiments of depth map restoration, pixel-level segmentation, and comparison between the estimated camera trajectory and the real camera trajectory are carried out on TUM dataset and real home scenes. The experimental results show that the proposed algorithm exhibits good real-time performance and robustness. ? 2022, Science Press. All right reserved.
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