This paper presents an incremental structural modeling approach that improves the precision and the stability of existing batch-based ones for sparse and noisy point clouds from visual simultaneous localization and mapping (SLAM). The main idea is to use the generating process of point clouds on SLAM effectively. First, a batch-based method is applied to point clouds that are incrementally generated from SLAM. Then, the temporal history of reconstructed geometric primitives is statistically analyzed to suppress incorrect reconstruction. The evaluation shows that both precision and stability are improved compared to an existing batch-based method, and the proposed method is suitable for real-time structural modeling.

中文翻译：

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稀疏视觉SLAM上的增量结构建模

本文提出了一种增量结构建模方法，该方法通过可视化同时定位和映射（SLAM）提高了现有基于批处理的稀疏和嘈杂点云的精度和稳定性。主要思想是在SLAM上有效利用点云的生成过程。首先，将基于批处理的方法应用于从SLAM增量生成的点云。然后，对重建的几何图元的时间历史进行统计分析，以抑制错误的重建。评估表明，与现有的基于批处理的方法相比，精度和稳定性都得到了改善，并且该方法适用于实时结构建模。