联合深度图聚类与目标检测的像素级分割算法

doi:10.16451/j.cnki.issn1003-6059.202202004

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Abstract 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.

Key words： Visual Simultaneous Localization and Mapping Semantic Simultaneous Localization and Mapping Image Clustering Object Detection

Received: 12 August 2021

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61872327), Project of Collaborative Innovation in Anhui Colleges and Universities(No.GXXT-2019-003), Open Fund of Key Laboratory of Flight Techniques and Flight Safety of Civil Aviation Administration of China(No.FZ2020KF02)

Corresponding Authors: FANG Baofu, Ph.D., associate professor. His research interests include intelligent robot systems.

About author:: ZHANG Xu, master student. His research interests include visual SLAM.WANG Hao, Ph.D., professor. His research interests include distributed intelligent systems and robots.

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Cite this article:

FANG Baofu,ZHANG Xu,WANG Hao. Pixel?Level Segmentation Algorithm Combining Depth Map Clustering and Object Detection[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 130-140.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202202004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I2/130

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