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Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Weijie Huang, Guoshan Zhang and Xiaowei Han, "Dense Mapping From an Accurate Tracking SLAM," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1565-1574, Nov. 2020. doi: 10.1109/JAS.2020.1003357
Citation: Weijie Huang, Guoshan Zhang and Xiaowei Han, "Dense Mapping From an Accurate Tracking SLAM," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1565-1574, Nov. 2020. doi: 10.1109/JAS.2020.1003357

Dense Mapping From an Accurate Tracking SLAM

doi: 10.1109/JAS.2020.1003357
Funds:  This work was supported by the National Natural Science Foundation of China (61473202)
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  • In recent years, reconstructing a sparse map from a simultaneous localization and mapping (SLAM) system on a conventional CPU has undergone remarkable progress. However, obtaining a dense map from the system often requires a high-performance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.


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    • Prior information from an accurate tracking SLAM is used to associate dense vertices between keyframes based on multithreaded processing and multi-threaded priority settings.
    • The angle change and position change of the associated vertices are constructed and examined to determine if they are within two setting ranges to remove outliers. The two ranges are designed by using a rotation angle histogram and a beam-based environment measurement model, respectively.
    • An adaptive weight is assigned to each inlier and the weighted fusion is implemented as the update process of the Kalman filter.
    • The surfaces of inliers are stored in a global hash table and a local hash table for fast data operation and data reuse.


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