As a fundamental task for intelligent robots, visual SLAM has made significant progress in recent years. However, robust SLAM in weakly textured environments remains a challenging task. In this paper, we present a novel visual Robust SLAM for Weak-Textured environments (RWT-SLAM) to address this problem. Unlike existing methods that use detector-based deep networks for interest point detection, we propose extracting distinctive features from a detector-free based network, namely LoFTR, to avoid the difficulty of manual annotations of feature points in weakly textured images. We generate multi-level feature vectors from LoFTR to form dense descriptors for each pixel in the input image. A keypoint localization component is then proposed to measure the saliency of the descriptors and select the distinctive pixels as keypoints. We integrate this new keypoint into the popular ORB-SLAM framework and compare it with the state-of-the-art methods. Extensive experiments on popular TUM RGB-D, OpenLORIS-Scene, as well as our own dataset are carried out. The results demonstrate the superior performance of our method in weakly textured environments.


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    Title :

    RWT-SLAM: Robust Visual SLAM for Weakly Textured Environments


    Contributors:
    Peng, Qihao (author) / Zhao, Xijun (author) / Dang, Ruina (author) / Xiang, Zhiyu (author)


    Publication date :

    2024-06-02


    Size :

    4689229 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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