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.

中文翻译：

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来自精确跟踪SLAM的密集映射

近年来，在常规CPU上从同时定位和映射（SLAM）系统重建稀疏映射的工作取得了显着进展。但是，从系统获得密集的地图通常需要高性能的GPU来加速计算。本文提出了一种密集映射方法，该方法可以消除异常值并使用CPU实时获得干净的3D模型。密集映射方法使用多线程技术处理关键帧并建立数据关联。通过更改关键帧之间关联顶点的检测，可以消除异常值。内在的隐式表面数据由截短的有符号距离函数表示，并与自适应权重融合。全局哈希表和本地哈希表用于存储和检索表面数据以供数据重用。